CN109657229A - A kind of intention assessment model generating method, intension recognizing method and device - Google Patents

A kind of intention assessment model generating method, intension recognizing method and device Download PDF

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CN109657229A
CN109657229A CN201811290240.4A CN201811290240A CN109657229A CN 109657229 A CN109657229 A CN 109657229A CN 201811290240 A CN201811290240 A CN 201811290240A CN 109657229 A CN109657229 A CN 109657229A
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vector
text
region
sample
word
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符文君
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Beijing QIYI Century Science and Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems

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  • Audiology, Speech & Language Pathology (AREA)
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Abstract

The present invention provides a kind of intention assessment model generating method, intension recognizing method and devices, belong to field of computer technology.Specifically, each sample text can be inputted initial intention assessment model by the generation method, and according to the sample areas vector in the corresponding each sample text region of each sample text, sample areas phonetic vector and sample areas label vector, determine the prediction intention value of each sample text, prediction intention value and true intention value based on sample text calculate penalty values, when penalty values within a preset range, using initial intention assessment model as intention assessment model, in this way, when carrying out intention assessment using the intention assessment model, it can be based on the region vector of statement text to be sorted, region phonetic vector and area label vector are identified, since region phonetic vector sum area label vector can reduce ambiguity deviation caused by accuracy existing for the word and domanial words of identification mistake, and then the standard of intention assessment can be improved True rate.

Description

A kind of intention assessment model generating method, intension recognizing method and device
Technical field
The invention belongs to field of computer technology, more particularly to a kind of intention assessment model generating method, intention assessment Method and device.
Background technique
With the fast development of computer technology, novel product of the intelligent assistant as a kind of combination machine intelligence and dialogue Form, by user be intended to understand based on, help user complete particular task, for user bring hommization service experience and Convenient for servicing.
Currently, intelligent assistant based on man machine language's interactive mode, in the prior art, usually obtains in sample text Term vector and context term vector are trained, and obtain intention assessment model, in this way, when carrying out intention assessment, Ke Yili With the term vector and context term vector of each word in the intention assessment model extraction of the training statement text to be sorted Matrix, then, term vector and context term vector matrix based on each word of extraction determine that user inputs corresponding meaning Figure, and then a series of behaviors and strategy are generated and execute, realize the interaction with user.But statement text to be sorted is often What speech recognition obtained is carried out to the voice of user's input, since the precision of speech recognition is limited, in this way, statement text to be sorted In it is possible that wrong word, simultaneously, during actual use, the word that will appear ambiguity may be understood in text, in turn It will lead to and be not inconsistent using the intention of intention assessment model identification and the practical intention of user of the training, it is intended that the accuracy rate of identification It is lower.
Summary of the invention
The present invention provides a kind of intention assessment model generating method, intension recognizing method and device, to solve to be intended to know The lower problem of other accuracy rate.
According to the present invention in a first aspect, provide a kind of intention assessment model generating method, this method comprises:
Each sample text that training sample is concentrated inputs initial intention assessment model；
According to sample areas vector, the sample areas phonetic in the corresponding each sample text region of each sample text Vector and sample areas label vector, and the initial intention assessment model is utilized, determine the pre- of each sample text Survey intention value；
The true intention value of prediction intention value and each sample text based on each sample text, described in calculating The penalty values of initial intention assessment model；
When the penalty values within a preset range, using the initial intention assessment model as intention assessment model.
Optionally, after the penalty values for calculating the initial intention assessment model, the method also includes:
When the penalty values are not in the preset range, the parameter of the initial intention assessment model is adjusted, and is based on The training sample set continues to train to initial intention assessment model adjusted.
Optionally, each sample text that training sample is concentrated inputs before initial intention assessment model, described Method further include:
The parameter of each layer in initial intention assessment model is initialized；Wherein, the initial intention assessment model packet Include region vector aligned layer, region vector attention layer, full articulamentum and maximum value softmax layers soft；
The parameter of the region vector aligned layer includes at least: the term vector of each sample word in each sample text, The context term vector matrix of phonetic term vector and context term vector matrix and each entity tag.
Optionally, the sample areas vector according to the corresponding each sample text region of each sample text, Sample areas phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, it determines each described The prediction intention value of sample text, comprising:
For each sample text, which is inputted into the region vector aligned layer, is based on the region Vector aligned layer divides the sample text, obtains at least one corresponding sample text region of the sample text；
For each sample text, extract the sample areas in the corresponding each sample text region of the sample text to Amount, sample areas phonetic vector and sample areas label vector；
Sample areas vector, sample areas based on the corresponding each sample text region of each sample text Phonetic vector and sample areas label vector determine the prediction intention value of each sample text.
Optionally, described for each sample text, extract the corresponding each sample text region of the sample text Sample areas vector, sample areas phonetic vector and sample areas label vector, comprising:
Term vector by the region vector aligned layer based on each sample word in each sample text region, Calculate the sample areas vector in each sample text region；
Pinyin word by the region vector aligned layer based on each sample word in each sample text region Vector calculates the sample areas phonetic vector in each sample text region；
The sample word for belonging to target domain in each sample text region is determined by the region vector aligned layer The corresponding entity tag of language, and based on the corresponding entity of sample word for belonging to target domain in each sample text region Label calculates the sample areas label vector in each sample text region.
Optionally, described that each sample word in each sample text region is based on by the region vector aligned layer The term vector of language calculates the sample areas vector in each sample text region, comprising:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the term vector of the core word and the context term vector square of the core word as core word Battle array；
By the context term vector matrix and each sample in text filed is abutted by the region vector aligned layer The term vector of word carries out the first dot product operation, and carries out maximum pond to the result of first dot product operation, obtains the sample This text filed region vector.
Optionally, described that each sample word in each sample text region is based on by the region vector aligned layer The phonetic term vector of language calculates the sample areas phonetic vector in each sample text region, comprising:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the phonetic term vector of the core word and the context pinyin word of the core word as core word Vector matrix；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each sample word carries out the second dot product operation, and carries out maximum pond to the result of second dot product operation Change, obtains the region phonetic vector in the sample text region.
Optionally, described based on the corresponding entity of sample word for belonging to target domain in each sample text region Label calculates the sample areas label vector in each sample text region, comprising:
For each sample text region, determined by the region vector aligned layer every in the sample text region The context term vector matrix of a entity tag；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer The term vector of each sample word carries out the operation of third dot product in region, and carries out to the result of third dot product operation maximum Chi Hua obtains the corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector in the sample text region.
Second aspect according to the present invention provides a kind of intension recognizing method, this method comprises:
According to the input information of user, statement text to be sorted is determined；
By the statement text input intention assessment model to be sorted, by the intention assessment model to described to be sorted It states text and carries out intention assessment, obtain the intention classification of the statement text to be sorted；Wherein, the intention assessment model is It is generated using method described in first aspect.
Optionally, described that intention assessment is carried out to the statement text to be sorted by the intention assessment model, it obtains The intention classification of the statement text to be sorted, comprising:
The statement text to be sorted is divided by the intention assessment model, obtains the statement text to be sorted This is corresponding, and at least one is text filed；
By the corresponding each text filed region of statement to be sorted text described in the intention assessment model extraction to Amount, region phonetic vector and area label vector；
By the intention assessment model based on each text filed region vector, region phonetic vector and area Domain label vector determines the intention classification of the statement text to be sorted.
Optionally, the intention assessment model includes region vector aligned layer；
It is described to pass through the corresponding each text filed area of statement to be sorted text described in the intention assessment model extraction Domain vector, region phonetic vector and area label vector, comprising:
By the region vector aligned layer based on it is each it is described it is text filed in each word term vector, calculate each The text filed region vector；
By the region vector aligned layer based on it is each it is described it is text filed in each word phonetic term vector, calculate Each text filed region phonetic vector；
By the region vector aligned layer determine it is each it is described it is text filed in belong to the domanial words pair of target domain The entity tag answered, and based on it is each it is described it is text filed in the corresponding entity tag of each domanial words, calculate each described Text filed area label vector.
Optionally, the intention assessment model further includes region vector attention layer, full articulamentum and softmax layers；
It is described by the intention assessment model based on each text filed region vector, region phonetic vector with And area label vector, determine the intention classification of the statement text to be sorted, comprising:
By the region vector aligned layer by each text filed region vector, region phonetic vector and area Domain label vector is spliced, and each text filed final area vector is obtained；
The weight of each text filed final area vector is determined by the region vector attention layer；
By the full articulamentum based on each text filed the final area vector and its weight, determine it is described to The class vector of classification statement text；
The intention classification of the statement text to be sorted is determined based on the class vector by described softmax layers.
Optionally, described that the statement text to be sorted is divided by the intention assessment model, it obtains described Statement text to be sorted is corresponding, and at least one is text filed, comprising:
Participle operation is carried out to the statement text to be sorted by the region vector aligned layer, is obtained described to be sorted The m word that statement text includes；
I-th to the N+i word in the m word is divided into a text by the region vector aligned layer Region, obtaining the statement text to be sorted, corresponding at least one is text filed；
Wherein, the i=1...m-N, the N and the m are the positive integer greater than 1.
Optionally, it is described by the region vector aligned layer based on it is each it is described it is text filed in each word word to Amount calculates each text filed region vector, comprising:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the term vector of the core word and the context term vector matrix of the core word are determined；
By the context term vector matrix and each word in text filed is abutted by the region vector aligned layer Term vector carry out the operation of the 4th dot product, and maximum pond is carried out to the result of the 4th dot product operation, obtains this article local area The region vector in domain.
Optionally, it is described by the region vector aligned layer based on it is each it is described it is text filed in each word phonetic Term vector calculates each text filed region phonetic vector, comprising:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the phonetic term vector of the core word and the context pinyin word vector matrix of the core word are determined；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, Obtain the region phonetic vector of this article one's respective area.
Optionally, it is described by the region vector aligned layer determine it is each it is described it is text filed in belong to target domain The corresponding entity tag of domanial words, comprising:
For each described text filed, preset target domain dictionary is based on by the region vector aligned layer, it will The word for belonging to the target domain dictionary in the word that this article one's respective area includes is determined as domanial words；
The corresponding label of each domanial words is searched in the target domain dictionary by the region vector aligned layer, Obtain the entity tag of each domanial words.
Optionally, it is described based on it is each it is described it is text filed in the corresponding entity tag of each domanial words, calculate each The text filed area label vector, comprising:
For each described text filed, each entity mark in this article one's respective area is determined by the region vector aligned layer The context term vector matrix of label；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer Each term vector carries out the operation of the 6th dot product in region, and carries out maximum pond to the result of the 6th dot product operation, obtains The corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector of this article one's respective area.
Optionally, the input information according to user, determines statement text to be sorted, comprising:
When the input information is voice messaging, the voice messaging is converted into text, and by the text and Formerly the corresponding text of input information is determined as statement text to be sorted；
Wherein, formerly input information is received in the preset time period before receiving the input information.
The third aspect according to the present invention, provides a kind of intention assessment model generating means, which includes:
Input module, each sample text for concentrating training sample input initial intention assessment model；
First determining module, for the sample areas according to the corresponding each sample text region of each sample text Vector, sample areas phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, it determines each The prediction intention value of the sample text；
Computing module, the true meaning for prediction intention value and each sample text based on each sample text Map values calculate the penalty values of the initial intention assessment model；
Second determining module, for when the penalty values within a preset range, using the initial intention assessment model as Intention assessment model.
Optionally, described device further include:
Module is adjusted, for working as the penalty values not in the preset range, adjusts the initial intention assessment model Parameter, and initial intention assessment model adjusted is continued to train based on the training sample set.
Optionally, described device further include:
Initialization module is initialized for the parameter to each layer in initial intention assessment model；Wherein, described initial Intention assessment model includes region vector aligned layer, region vector attention layer, full articulamentum and soft maximum value softmax Layer；
The parameter of the region vector aligned layer includes at least: the term vector of each sample word in each sample text, The context term vector matrix of phonetic term vector and context term vector matrix and each entity tag.
Optionally, first determining module, comprising:
First divides submodule, for for each sample text, which to be inputted the region vector Aligned layer divides the sample text based on the region vector aligned layer, obtains the sample text corresponding at least one A sample text region；
First extracting sub-module, for extracting the corresponding each sample of the sample text for each sample text Text filed sample areas vector, sample areas phonetic vector and sample areas label vector；
First determines submodule, for the sample based on the corresponding each sample text region of each sample text One's respective area vector, sample areas phonetic vector and sample areas label vector determine the prediction meaning of each sample text Map values.
Optionally, first extracting sub-module, comprising:
First computing unit, it is each in each sample text region for being based on by the region vector aligned layer The term vector of sample word calculates the sample areas vector in each sample text region；
Second computing unit, it is each in each sample text region for being based on by the region vector aligned layer The phonetic term vector of sample word calculates the sample areas phonetic vector in each sample text region；
Third computing unit belongs to for being determined in each sample text region by the region vector aligned layer The corresponding entity tag of sample word of target domain, and based on the sample for belonging to target domain in each sample text region The corresponding entity tag of this word calculates the sample areas label vector in each sample text region.
Optionally, first computing unit, is used for:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the term vector of the core word and the context term vector square of the core word as core word Battle array；
By the context term vector matrix and each sample in text filed is abutted by the region vector aligned layer The term vector of word carries out the first dot product operation, and carries out maximum pond to the result of first dot product operation, obtains the sample This text filed region vector.
Optionally, second computing unit, is used for:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the phonetic term vector of the core word and the context pinyin word of the core word as core word Vector matrix；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each sample word carries out the second dot product operation, and carries out maximum pond to the result of second dot product operation Change, obtains the region phonetic vector in the sample text region.
Optionally, the third computing unit, is used for:
For each sample text region, determined by the region vector aligned layer every in the sample text region The context term vector matrix of a entity tag；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer The term vector of each sample word carries out the operation of third dot product in region, and carries out to the result of third dot product operation maximum Chi Hua obtains the corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector in the sample text region.
Fourth aspect according to the present invention, provides a kind of intention assessment device, which includes:
Third determining module determines statement text to be sorted for the input information according to user；
Identification module, for passing through the intention assessment mould for the statement text input intention assessment model to be sorted Type carries out intention assessment to the statement text to be sorted, obtains the intention classification of the statement text to be sorted；Wherein, described Intention assessment model is generated using device described in the third aspect.
Optionally, the identification module, comprising:
Second divides submodule, for being divided by the intention assessment model to the statement text to be sorted, Obtaining the statement text to be sorted, corresponding at least one is text filed；
Second extracting sub-module, for corresponding every by statement text to be sorted described in the intention assessment model extraction A text filed region vector, region phonetic vector and area label vector；
Second determine submodule, for by the intention assessment model based on each text filed region to Amount, region phonetic vector and area label vector determine the intention classification of the statement text to be sorted.
Optionally, the intention assessment model includes region vector aligned layer；
Second extracting sub-module, comprising:
4th computing unit, for by the region vector aligned layer be based on it is each it is described it is text filed in each word Term vector, calculate each text filed region vector；
5th computing unit, for by the region vector aligned layer be based on it is each it is described it is text filed in each word Phonetic term vector, calculate each text filed region phonetic vector；
6th computing unit, for by the region vector aligned layer determine it is each it is described it is text filed in belong to target The corresponding entity tag of the domanial words in field, and based on it is each it is described it is text filed in the corresponding entity mark of each domanial words Label calculate each text filed area label vector.
Optionally, the intention assessment model further includes region vector attention layer, full articulamentum and softmax layers；
Described second determines submodule, is used for:
By the region vector aligned layer by each text filed region vector, region phonetic vector and area Domain label vector is spliced, and each text filed final area vector is obtained；
The weight of each text filed final area vector is determined by the region vector attention layer；
By the full articulamentum based on each text filed the final area vector and its weight, determine it is described to The class vector of classification statement text；
The intention classification of the statement text to be sorted is determined based on the class vector by described softmax layers.
Optionally, described second submodule is divided, is used for:
Participle operation is carried out to the statement text to be sorted by the region vector aligned layer, is obtained described to be sorted The m word that statement text includes；
I-th to the N+i word in the m word is divided into a text by the region vector aligned layer Region, obtaining the statement text to be sorted, corresponding at least one is text filed；
Wherein, the i=1...m-N, the N and the m are the positive integer greater than 1.
Optionally, the 4th computing unit, is used for:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the term vector of the core word and the context term vector matrix of the core word are determined；
By the context term vector matrix and each word in text filed is abutted by the region vector aligned layer Term vector carry out the operation of the 4th dot product, and maximum pond is carried out to the result of the 4th dot product operation, obtains this article local area The region vector in domain.
Optionally, the 5th computing unit, is used for:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the phonetic term vector of the core word and the context pinyin word vector matrix of the core word are determined；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, Obtain the region phonetic vector of this article one's respective area.
Optionally, the 6th computing unit, is used for:
For each described text filed, preset target domain dictionary is based on by the region vector aligned layer, it will The word for belonging to the target domain dictionary in the word that this article one's respective area includes is determined as domanial words；
The corresponding label of each domanial words is searched in the target domain dictionary by the region vector aligned layer, Obtain the entity tag of each domanial words.
Optionally, the 6th computing unit, is used for:
For each described text filed, each entity mark in this article one's respective area is determined by the region vector aligned layer The context term vector matrix of label；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer Each term vector carries out the operation of the 6th dot product in region, and carries out maximum pond to the result of the 6th dot product operation, obtains The corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector of this article one's respective area.
Optionally, the third determining module, is used for:
When the input information is voice messaging, the voice messaging is converted into text, and by the text and Formerly the corresponding text of input information is determined as statement text to be sorted；
Wherein, formerly input information is received in the preset time period before receiving the input information.
For first technology, the present invention has following advantage: each sample text that training sample can be concentrated inputs Initial intention assessment model, then, according to sample areas vector, the sample in the corresponding each sample text region of each sample text One's respective area phonetic vector and sample areas label vector, and initial intention assessment model is utilized, determine each sample text It predicts intention value, then the prediction intention value based on each sample text and the true intention value of each sample text, calculates The penalty values of initial intention assessment model, when penalty values within a preset range, can be using initial intention assessment model as intention Identification model.In the embodiment of the present invention, by extract the corresponding each sample text region of sample text sample areas vector, Sample areas phonetic vector and sample areas label vector generate intention assessment model, so that in subsequent use process, benefit It, can be literary based on the statement to be sorted with the intention assessment model of the generation when treating classification statement text and carrying out intention assessment This region vector, region phonetic vector and area label vector are identified, since region phonetic vector can reduce Occur word deviation caused by classification accuracy of identification mistake in statement text to be sorted, it can be with by area label vector To domanial words, there are ambiguities to lead to classification results and the practical deviation for being intended to occur of user for reduction, and then intention can be improved and know Other accuracy rate.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention, And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 is a kind of step flow chart of intention assessment model generating method provided in an embodiment of the present invention；
Fig. 2 is the step flow chart of another intention assessment model generating method provided in an embodiment of the present invention；
Fig. 3 is a kind of step flow chart of intension recognizing method provided in an embodiment of the present invention；
Fig. 4-1 is the step flow chart of another intension recognizing method provided in an embodiment of the present invention；
Fig. 4-2 is that the embodiment of the present invention provides a kind of structural schematic diagram of intention assessment model；
Fig. 5 is the step flow chart of another intension recognizing method provided in an embodiment of the present invention；
Fig. 6 is a kind of block diagram of intention assessment model generating means provided in an embodiment of the present invention；
Fig. 7 is a kind of block diagram of intention assessment device provided in an embodiment of the present invention.
Specific embodiment
The exemplary embodiment that the present invention will be described in more detail below with reference to accompanying drawings.Although showing the present invention in attached drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the present invention without should be by embodiments set forth here It is limited.It is to be able to thoroughly understand the present invention on the contrary, providing these embodiments, and can be by the scope of the present invention It is fully disclosed to those skilled in the art.
Fig. 1 is a kind of step flow chart of intention assessment model generating method provided in an embodiment of the present invention, such as Fig. 1 institute Show, this method may include:
Step 101, each sample text for concentrating training sample input initial intention assessment model.
In the embodiment of the present invention, it is preparatory according to actual needs that the sample text which concentrates can be developer It chooses, exemplary, when which can be user's progress voice control, the sentence that may be used, the present invention is implemented Example is not construed as limiting this.Further, which it is preparatory based on neural network model to can be developer Building, which may include multilayered structure, and different processing may be implemented in every layer of structure.
Step 102, according to sample areas vector, the sample in the corresponding each sample text region of each sample text Region phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, determine each sample The prediction intention value of text.
In the embodiment of the present invention, it can use initial intention assessment model and the sample text of input handled, specifically , initial intention assessment model can first obtain the corresponding sample text region of each sample text, then determine each sample Sample areas vector, sample areas phonetic vector and the sample areas label in the corresponding each sample text region of text to Amount, wherein sample areas vector can be based on the term vector determination in sample text region, and sample phonetic region vector can Being determined based on the phonetic term vector in sample text region, sample areas label vector be can be based on sample text Belong to the corresponding entity tag determination of sample word of target domain in region, then, each sample text pair can be based on Sample areas vector, sample areas phonetic vector and the sample areas label vector answered, determine the prediction of each sample text Intention value.
In this way, being spelled by the sample areas vector in the corresponding each sample text region of extraction sample text, sample areas Sound vector and sample areas label vector generate intention assessment model, in subsequent use process, utilize the intention of the generation Identification model treat classification statement text carry out intention assessment when, can based on this it is to be sorted statement text region vector, Region phonetic vector and area label vector are identified, since region phonetic vector can reduce statement text to be sorted The middle word deviation caused by classification accuracy for identification mistake occur, can be reduced by area label vector to domanial words There are ambiguities to lead to classification results and the practical deviation for being intended to occur of user, it is thus possible to improve the accuracy rate of intention assessment
The true intention value of step 103, the prediction intention value based on each sample text and each sample text, Calculate the penalty values of the initial intention assessment model.
In the embodiment of the present invention, the true intention value of each sample text can be and predefine when collecting sample text , which can indicate the extent of deviation between the prediction intention value of sample text and the true intention value of sample text.
Step 104, when the penalty values within a preset range, using initial intention assessment model as intention assessment model.
In the embodiment of the present invention, which can set according to practical application scene and actual demand, and the present invention is real It is without restriction to its to apply example.Further, if penalty values within a preset range, it may be considered that each sample text is pre- The deviation surveyed between intention value and its true intention value is very small, at this point it is possible to think the prediction intention value of sample text with Its true intention value is consistent, and the true intention which can correctly predict text correspondingly can Using by the initial intention assessment model as intention assessment model.
In conclusion a kind of intention assessment model generating method provided in an embodiment of the present invention, it can be by training sample set In each sample text input initial intention assessment model, then, according to the corresponding each sample text of each sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region, and utilize initial intention assessment mould Type determines the prediction intention value of each sample text, then the prediction intention value based on each sample text and each sample The true intention value of text calculates the penalty values of initial intention assessment model, when penalty values within a preset range, will can initially anticipate Figure identification model is as intention assessment model.In the embodiment of the present invention, by extracting the corresponding each sample text of sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region generates intention assessment model, makes It obtains in subsequent use process, using the intention assessment model of the generation when treating classification statement text progress intention assessment, energy It is enough to be identified based on region vector, region phonetic vector and the area label vector of the statement text to be sorted, due to Region phonetic vector, which can reduce in statement text to be sorted, there is word deviation caused by classification accuracy of identification mistake, By area label vector can reduce to domanial words there are ambiguity cause classification results and user it is practical be intended to occur it is inclined Difference, and then the accuracy rate of intention assessment can be improved.
Fig. 2 is the step flow chart of another intention assessment model generating method provided in an embodiment of the present invention, such as Fig. 2 institute Show, this method may include:
Step 201 initializes the parameter of each layer in initial intention assessment model.
In this step, initial intention assessment model may include multilayered structure, which may include Region vector aligned layer, region vector attention layer, full articulamentum and softmax layers.Further, the parameter of each layer can be with Be the realization according to needed for every layer function it is pre-selected, specifically, for region vector aligned layer, the parameter of this layer is at least wrapped It includes: the term vector of each sample word, phonetic term vector and context term vector matrix in each sample text, and, often The context term vector matrix of a entity tag, wherein the sample word comprising corresponding different entities label in sample word.Tool Body, in initialization, it can be and first generate initial value of the number as parameter at random, or being based on experience is each ginseng Number setting initial value, for example, be directed to the context term vector matrix of sample word, can in conjunction with the word it is common indicate up and down Literary context sets initial value, and the embodiment of the present invention is not construed as limiting this.
Step 202, each sample text for concentrating training sample input initial intention assessment model.
Specifically, the implementation of this step can refer to above-mentioned steps 101, the embodiment of the present invention is not construed as limiting this.
Step 203, according to sample areas vector, the sample in the corresponding each sample text region of each sample text Region phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, determine each sample The prediction intention value of text.
Specifically, this step can be realized by 2031~step 2033 of following step:
The sample text input region vector aligned layer is based on each sample text by step 2031 The region vector aligned layer divides the sample text, obtains at least one corresponding sample text area of the sample text Domain.
In this step, participle operation first can be carried out to the sample text, obtain the p sample word that the sample text includes Language, wherein word segmentation processing refers to the process that continuous word sequence is reassembled into word sequence according to certain specification.Right Sample text carry out participle operation when, can using the segmenting method based on string matching, the segmenting method based on understanding and Segmenting method, etc. based on statistics, the embodiment of the present invention is not construed as limiting this.It then, can will be in the p sample word Xth to the Q+x sample word is divided into a sample text region, obtains corresponding at least one sample text of the sample text One's respective area.Wherein, x=1...n-Q, n and Q are the positive integer greater than 1.Wherein, the occurrence of Q can be according to practical need Ask preset, the embodiment of the present invention is not construed as limiting this.
Step 2032, for each sample text, extract the corresponding each sample text region of the sample text Sample areas vector, sample areas phonetic vector and sample areas label vector.
Specifically, this step can be realized by following sub-steps (1)~sub-step (3):
Sub-step (1): word by region vector aligned layer based on each sample word in each sample text region to Amount, calculates the sample areas vector in each sample text region.
It, can will be in the sample text region by region vector aligned layer specifically, for each sample text region Between position sample word as core word, determine the term vector of the core word and the context term vector square of the core word Battle array.Specifically, when determining the sample word in sample text region middle position, the sample that can include in sample text region When this word number is odd number, determine that (n+1)/2 sample word in the sample text region is the sample word in middle position, When the sample word number for including in sample text region is even number, the n-th/2 sample word in the sample text region is determined Or n-th/2+1 sample word is the sample word in middle position.Further, in the term vector and core for determining core word When the context term vector matrix of heart word, the core word can be searched in the preset parameter of region vector aligned layer Term vector and context term vector matrix, it is then possible to pass through region vector aligned layer for context term vector matrix and neighbour The term vector for connecing each word in sample text region carries out the first dot product operation, and carries out to the result of first dot product operation Maximum pond, that is, take the maximum point of local acceptance region intermediate value, obtain the region vector in the sample text region.
Sub-step (2): each sample word in each sample text region is based on by the region vector aligned layer The phonetic term vector of language calculates the sample areas phonetic vector in each sample text region.
It, can will be in the sample text region by region vector aligned layer specifically, for each sample text region Between position sample word as core word, determine the phonetic term vector of the core word and the context pinyin word of the core word Vector matrix.It correspondingly, can in the context pinyin word vector matrix of the phonetic term vector and core word that determine core word With searched in the preset parameter of region vector aligned layer the core word phonetic term vector and context pinyin word to Moment matrix, it is then possible to by context pinyin word vector matrix and be abutted each in text filed by region vector aligned layer The phonetic term vector of sample word carries out the second dot product operation, and carries out maximum pond to the result of the second dot product operation, that is, takes The local maximum point of acceptance region intermediate value, obtains the region phonetic vector in the sample text region.
Sub-step (3): it is determined by the region vector aligned layer and belongs to target neck in each sample text region The corresponding entity tag of sample word in domain, and based on the sample word for belonging to target domain in each sample text region Corresponding entity tag calculates the sample areas label vector in each sample text region.
Specifically, can determine the sample text region by region vector aligned layer for each sample text region In each entity tag context term vector matrix, then, by region vector aligned layer will each entity tag up and down Cliction vector matrix and the term vector for abutting each sample word in text filed carry out the operation of third dot product, and to third dot product The result of operation carries out maximum pond, obtain the corresponding maximum pond of each entity tag as a result, finally, pass through region to Amount aligned layer calculates the average value of the corresponding maximum pond result of each entity tag, obtains the region mark in the sample text region Sign term vector.Wherein, the first dot product operation above-mentioned, the operation of the second dot product and the operation of third dot product are referred to the prior art The mode of middle vector dot carries out, each sample word in the context term vector matrix of entity tag, adjacent sample text region The term vector and phonetic term vector of language can be to be obtained from the preset parameter of region vector aligned layer, the present invention This will not be repeated here for embodiment.
Step 2033, the sample areas based on the corresponding each sample text region of each sample text to Amount, sample areas phonetic vector and sample areas label vector, determine the prediction intention value of each sample text.
Specifically, region vector aligned layer can be first passed through by the sample areas in each sample text region in this step Vector, sample areas phonetic vector and sample areas label vector are spliced, and the sample in each sample text region is obtained Then final area vector determines the sample final area vector in each sample text region by region vector attention layer Weight, specifically, region vector attention layer can use attention mechanism algorithm, according to sample final area vector sum sample Relevance between text determines each sample final area vector weight.Then, each sample can be based on by full articulamentum This text filed sample final area vector and its weight determines the sample classification vector of each sample text, finally, passing through The softmax layers of prediction intention value for determining sample text based on sample classification vector, wherein the prediction intention value can be sample It is intended to the corresponding value of classification belonging to text, specifically, softmax layers can calculate the sample text for each sample text The distance between corresponding sample classification vector vector corresponding with each intention classification, apart from smaller, it may be considered that the sample The probability that this text belongs to the intention classification is bigger, and the intention classification which can finally be corresponded to maximum probability is corresponding Value, the prediction intention value as the sample text.
The true intention value of step 204, the prediction intention value based on each sample text and each sample sentence, Calculate the penalty values of the initial intention assessment model.
Specifically, the implementation of this step can refer to above-mentioned steps 104, the embodiment of the present invention is not construed as limiting this.
In this step, loss function can be used as using entropy function is intersected, by the prediction intention value of each sample text and True intention value is substituted into the cross entropy loss function and is calculated, using the value being calculated as penalty values, further, It can be calculated using unknown losses function, the embodiment of the present invention is not construed as limiting this.It is of course also possible to calculate each sample Euclidean distance between the prediction intention value and true intention value of text, obtains the corresponding penalty values of each sample text, then The mean value for calculating the corresponding penalty values of each text obtains the penalty values of initial intention assessment model.
Step 205, when the penalty values within a preset range, using the initial intention assessment model as intention assessment mould Type.
Specifically, the implementation of this step can refer to above-mentioned steps 105, the embodiment of the present invention is not construed as limiting this.
Step 206, when the penalty values are not in the preset range, adjust the ginseng of the initial intention assessment model Number, and initial intention assessment model adjusted is continued to train based on the training sample set.
In this step, if penalty values are not within a preset range, it may be considered that the prediction intention value of each sample text Deviation between its true intention value is larger, which can't correctly predict the true of text Therefore sincere figure can be adjusted according to parameter of the preset step-length to initial intention assessment model, and want to propagate after utilizing to calculate Method continues to train to initial intention assessment model adjusted, that is, by modifying parameter, is iterated training, makes initially to be intended to The recognition result of identification model is more nearly legitimate reading, correspondingly, during mostly wheel repetitive exercise, if a certain wheel is first The penalty values control of beginning intention assessment model within a preset range, then can be using the initial intention assessment model of the wheel as intention Identification model.
In conclusion another kind intention assessment model generating method provided in an embodiment of the present invention, it can be by training sample The each sample text concentrated inputs initial intention assessment model, then, according to the corresponding each sample text of each sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector of one's respective area, and utilize initial intention assessment Model determines the prediction intention value of each sample text, then the prediction intention value based on each sample text and each sample The true intention value of this text calculates the penalty values of initial intention assessment model, when penalty values within a preset range, can will be initial Intention assessment model as intention assessment model, when penalty values not within a preset range, initial intention assessment model can be adjusted Parameter continues to train.In the embodiment of the present invention, by the sample areas for extracting the corresponding each sample text region of sample text Vector, sample areas phonetic vector and sample areas label vector generate intention assessment model, so that subsequent use process In, it, can be to be sorted based on this using the intention assessment model of the generation when treating classification statement text progress intention assessment Region vector, region phonetic vector and the area label vector of text are stated to be identified, since region phonetic vector can With reduce it is to be sorted statement text in occur identification mistake word deviation caused by classification accuracy, by area label to Amount can be reduced to domanial words there are the deviation that ambiguity leads to classification results and the practical intention appearance of user, and then can be improved The accuracy rate of intention assessment.
Fig. 3 is a kind of step flow chart of intension recognizing method provided in an embodiment of the present invention, as shown in figure 3, this method May include:
Step 301, the input information according to user, determine statement text to be sorted.
In the embodiment of the present invention, which can be the voice of user's input, for example, user inputs language to client Sound " search fellow No.9 door ", then the voice " search fellow No.9 door " is input information.It is possible to further extract input voice Vocal print feature, the vocal print feature for being then based on extraction, which is realized, is converted to text for the voice that user inputs.For example, terminal by pair Voice " search fellow No.9 door " is converted, and the text being converted to " search fellow No.9 door " can be determined as statement text to be sorted This.Certainly, which is also possible to the text of user's input, and correspondingly, the text of user input is table to be sorted State text.Wherein, which can be mounted in the electronics such as terminal, mobile phone, computer, set-top box, television set, intelligent glasses and set Standby upper client.
Step 302, by the statement text input intention assessment model to be sorted, by the intention assessment model to institute It states statement text to be sorted and carries out intention assessment, obtain the intention classification of the statement text to be sorted.
In this step, which, which can be, utilizes the generation of aforementioned intention assessment model generating method, due to The intention assessment model is by extracting the sample areas vector in the corresponding each sample text region of sample text, sample areas What phonetic vector and sample areas label vector generated, in this way, in this step, using the intention assessment model to be sorted It, can be based on region vector, region phonetic vector and the area of the statement text to be sorted when stating text progress intention assessment Domain label vector is identified the word of identification mistake occur since region phonetic vector can reduce in statement text to be sorted Language deviation caused by classification accuracy can be reduced to cause to classify there are ambiguity to domanial words by area label vector and be tied Fruit and the practical deviation for being intended to occur of user, and then the accuracy rate of intention assessment can be improved.
In conclusion a kind of intension recognizing method provided in an embodiment of the present invention, it can be according to the input information of user, really Fixed statement text to be sorted is treated point then by statement text input intention assessment model to be sorted by intention assessment model Class states text and carries out intention assessment, obtains the intention classification of statement text to be sorted, wherein the intention assessment model is to pass through Extract sample areas vector, sample areas phonetic vector and the sample areas in the corresponding each sample text region of sample text Label vector, and the generation of this these vector based on extraction, therefore, in the embodiment of the present invention, utilize the intention assessment model It, can be based on region vector, the region phonetic of the statement text to be sorted when treating classification statement text progress intention assessment Vector and area label vector are identified, are known since region phonetic vector can reduce in statement text to be sorted Not wrong word deviation caused by classification accuracy, can reduce that there are ambiguities to domanial words by area label vector Lead to classification results and the practical deviation for being intended to occur of user, and then the accuracy rate of intention assessment can be improved.
Fig. 4-1 is the step flow chart of another intension recognizing method provided in an embodiment of the present invention, as shown in Fig. 4-1, This method may include:
Step 401, the input information according to user, determine statement text to be sorted.
In actual application scenarios, user may repeatedly be interacted with terminal whithin a period of time, and two people are at one section There may be association between time interior more words of speaking, therefore, in this step, terminal is determining statement text to be sorted When, can input information be voice messaging when, the voice messaging is first converted into text, then by the text and formerly it is defeated Enter the corresponding text of information as statement text to be sorted, wherein formerly input information can be receive input information it It is received in preceding preset time period, which can be presets according to actual needs, and exemplary, this is default Period can be 1 minute, which may be half a minute, and the embodiment of the present invention is not construed as limiting this.The present invention In embodiment, pass through the corresponding text of input information that the user received is inputted into the corresponding text of information and is received before This can make full use of the relevance between language as statement text to be sorted, increase entrained by statement text to be sorted Effective information, and then improve the accuracy rate that intention assessment is carried out based on the statement text to be sorted.
Step 402 divides the statement text to be sorted by the intention assessment model, obtains described wait divide Class states text, and corresponding at least one is text filed.
In practical application scene, statement text to be sorted is often the sentence being made of multiple words, and in sentence not The meaning stated with word is often different, and each word is not also identical to the semantic influence degree of entire sentence, therefore, this In inventive embodiments, it is text filed statement text to be sorted can be divided at least one, wherein each text filed middle packet Multiple words are included.In this way, it is multiple text filed by the way that statement text to be sorted to be divided into, so that can be right in subsequent process It is each it is text filed be respectively processed analysis, and then can more accurately analyze language represented by entire statement text to be sorted Justice.
Participle operation is carried out specifically, intention assessment model can be first passed through and treat classification statement text, is obtained to be sorted The m word that statement text includes.In this step, word segmentation processing is referred to continuous word sequence according to certain specification again It is combined into the process of word sequence.It, can be using based on string matching when treating classification statement text and carrying out participle operation Segmenting method, the segmenting method based on understanding and segmenting method based on statistics, etc., the embodiment of the present invention does not limit this It is fixed.
Then, by i-th in m word to the N+i word be divided into one it is text filed, obtain statement to be sorted text This is corresponding, and at least one is text filed.
In this step, i=1...m-N, m, N are the positive integer greater than 1.Wherein, the occurrence of N can be according to reality Demand is preset, exemplary, it is assumed that N 2 treats classification chart text and carries out after segmenting operation operation, obtains 5 words Language: a, b, c, d, e, then can be by the 1st to the 3rd word, that is, word a, b, c are divided into one text filed [a, b, c], By the 2nd to the 4th word, that is, word b, c, d are divided into one text filed [b, c, d], by the 3rd to the 5th word, that is, word Language c, d, e are divided into one text filed [c, d, e], obtain 3 it is text filed.
It is step 403, corresponding each text filed by statement text to be sorted described in the intention assessment model extraction Region vector, region phonetic vector and area label vector.
In this step, which may include region vector aligned layer, correspondingly, can pass through following sub-steps Suddenly (4)~sub-step (6) is realized:
Sub-step (4): by the region vector aligned layer based on it is each it is described it is text filed in each word word to Amount calculates each text filed region vector.
Specifically, region vector aligned layer can be first passed through by text filed middle position for each text filed Word determines the term vector of core word and the context term vector matrix of core word, wherein determining text as core word When the word in region middle position, when the word number that can include in text filed is odd number, the is determined in this article one's respective area (n+1)/2 word is that the word in middle position determines this article one's respective area when the word number for including is even number in text filed In the n-th/2 word or n-th/2+1 word be middle position word, in the term vector and core word for determining core word Context term vector matrix when, due to training intention assessment model when, it has been determined that go out different terms term vector and Phonetic term vector, the context term vector matrix of word and context pinyin word vector matrix therefore, can be in this step The corresponding term vector of each core word and context term vector matrix are searched from the parameter of region vector aligned layer.
Then, each word in text filed by context term vector matrix and can be abutted by region vector aligned layer Term vector carry out the 4th dot product operation, that is, take the maximum point of local acceptance region intermediate value and to the 4th dot product operation result into Row maximum pond, obtains the region vector of this article one's respective area.Wherein, it is adjacent it is text filed can be with before this article one's respective area and Later adjacent text filed, it is adjacent it is text filed in the term vector of each word can be the ginseng from region vector aligned layer Lookup obtains in number, and the mode that the operation of the 4th dot product is referred to vector dot in the prior art carries out, and the present invention implements to exist This is not repeated them here.
Sub-step (5): by the region vector aligned layer based on it is each it is described it is text filed in each word phonetic Term vector calculates each text filed region phonetic vector.
It, can be by region vector aligned layer by this article one's respective area middle position for each text filed in this step Word as core word, determine the phonetic term vector of core word and the context pinyin word vector matrix of core word, specifically , the corresponding phonetic term vector of each core word and context pinyin word can be searched from the parameter of region vector aligned layer Vector matrix.
Then, it by context pinyin word vector matrix and can be abutted each in text filed by region vector aligned layer The phonetic term vector of word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, obtains this article The region phonetic vector of one's respective area.Wherein, the phonetic term vector for abutting each word in text filed can be from region vector Lookup obtains in the parameter of aligned layer, and the mode that the operation of the 5th dot product is referred to vector dot in the prior art carries out, This will not be repeated here for present invention implementation.
Due to different word may corresponding phonetic it is identical, for example, phonetic of the word " fellow No.9's door " with " wine is laid one's hand on " It is identical, but the two is to indicate the different terms of different meanings, therefore, there are the words of transcription error in statement text to be sorted When language, information represented by the word that is carried of region vector that is determined based on the term vector of word cannot be represented accurately Meaning represented by text filed middle word.And the intention assessment model in the embodiment of the present invention is true using the term vector of word While determining region vector, the phonetic term vector in region can be also determined using the phonetic term vector of phonetic, since phonetic can The pronunciation of the accurate voice for indicating user's input, therefore, the region phonetic vector determined based on the phonetic term vector of phonetic is held Information represented by the phonetic of load can accurately represent meaning represented by text filed middle phonetic, so correct due to Intention assessment deviation caused by speech recognition.
Sub-step (6): by the region vector aligned layer determine it is each it is described it is text filed in belong to target domain The corresponding entity tag of domanial words, and based on it is each it is described it is text filed in the corresponding entity tag of each domanial words, meter Calculate each text filed area label vector.
In the embodiment of the present invention, target domain can indicate field belonging to statement text to be sorted, due to same word There is different meanings in different fields, can thus exist and understand ambiguity, for example, word " apple " may indicate hand Machine, it is also possible to indicate fruit.Therefore, in this step, preset target domain dictionary can be based on by region vector aligned layer, The word for belonging to target domain dictionary in word that this article one's respective area includes is determined as domanial words, then, by region to Amount aligned layer searches the corresponding label of each domanial words in target domain dictionary, obtains the entity mark of each domanial words Label.It wherein, may include all words for belonging to target domain and the corresponding entity of each word in the target domain dictionary Label, wherein entity tag can be used to indicate that actual content represented by the domanial words, exemplary, it is assumed that this article local area Domanial words in domain are " fellow No.9's door ", and the domanial words corresponding label in target domain dictionary is " TV play ", then The entity tag of the available domanial words is " TV play ".
It is possible to further determine the cliction up and down of each entity tag in this article one's respective area by region vector aligned layer Vector matrix by the context term vector matrix of each entity tag and is abutted every in text filed by region vector aligned layer A term vector carries out the operation of the 6th dot product, and carries out maximum pond to the result of the 6th dot product operation, obtains each entity tag Corresponding maximum pond is as a result, finally, calculate the corresponding maximum pond result of each entity tag by region vector aligned layer Average value, obtain the area label term vector of this article one's respective area.Wherein, the context term vector matrix of entity tag can be It searches and obtains from the parameter of region vector aligned layer, the operation of the 6th dot product is referred to vector dot in the prior art Mode carries out, and this will not be repeated here for present invention implementation.
In the embodiment of the present invention, the corresponding entity of each domanial words in text filed is based on by region vector aligned layer Label determines text filed area label vector, since the entity tag of domanial words can accurately define domanial words Represented actual content eliminates the understanding ambiguity of domanial words, and therefore, the area label vector determined based on entity tag can With the more accurate meaning represented by text filed middle word that indicates, and then corrects and understand caused by ambiguity since word exists Intention assessment deviation.
Step 404, by the intention assessment model based on each text filed region vector, region phonetic to Amount and area label vector determine the intention classification of the statement text to be sorted.
In this step, which can also include region vector attention layer, full articulamentum and softmax Layer, Fig. 4-2 is that the embodiment of the present invention provides a kind of structural schematic diagram of intention assessment model, as shown in the Fig. 4-2, the intention assessment Model may include region vector aligned layer, region vector attention layer, full articulamentum and softmax layers in total.Correspondingly, It can be realized by following sub-steps (7)~sub-step (8):
Sub-step (7): by the region vector aligned layer by each text filed region vector, region phonetic Vector and area label vector are spliced, and each text filed final area vector is obtained.
Specifically, can be by region vector by text filed region vector, region phonetic vector and area label Vector Groups are combined into a vector, obtain the final area vector of this article one's respective area.Combination when, region vector, region phonetic to Amount and the sequence of area label vector can be preset according to actual needs, and the embodiment of the present invention is not construed as limiting this.Show Example, it is assumed that text filed region vector, region phonetic vector and area label vector is the vector of 100 dimensions, is passed through Splice the final area vector that available dimension is 300.
Sub-step (8): each text filed final area vector is determined by the region vector attention layer Weight.
Wherein, region vector attention layer can be using attention mechanism algorithm, wherein attention (Attention) For the essence of mechanism from human visual attention's mechanism, visual attention mechanism is at brain signal specific to human vision Reason mechanism, human vision obtain the target area for needing to pay close attention to, that is, general described by quickly scanning global image ' s focus of attention, then to the more attention resources of this regional inputs, with obtain it is more required for concern targets details Information, and inhibit other garbages.Therefore, attention mechanism algorithm be based on simulation human attention mechanism and establish one Kind network algorithm, region vector attention layer can be based on attention mechanism, capture final area vector sum statement text to be sorted Relevance between this, the relevance can be attention weight, by by attention weight distribution to corresponding final area To get to the weight for applying attention mechanism on vector, since the weight includes final area vector sum table to be sorted State the relevance between text, therefore, it is subsequent using the weight carry out as classification in application, enable classification results more It is accurate to add.
Specifically, attention mechanism algorithm used by the vector attention layer of region can be indicated by following formula:
Wherein, αiIndicate the weight of final area vector, usIndicate the corresponding matrix mapping of final area vector, ui= tanh(Whi+bs) indicate the corresponding context-aware matrix mapping of the final area vector, hiIndicate final area vector, W, bsIt is pre- First trained parameter.
Sub-step (9): by the full articulamentum based on each text filed the final area vector and its weight, Determine the class vector of the statement text to be sorted.
In this step, full articulamentum can be calculated all based on each text filed final area vector and its weight Text filed weighted sum, that is, the sum of products of each text filed final area vector and its weight obtains table to be sorted State the class vector of text.Exemplary, class vector v can be indicated are as follows:
Sub-step (10): the statement text to be sorted is determined based on the class vector by described softmax layers It is intended to classification.
It, can be by between softmax layers of calculating class vector vector corresponding with each intention classification in this step Distance, wherein apart from smaller, it may be considered that the probability that the statement text to be sorted belongs to the intention classification is bigger, finally, can Intention classification the statement text to be sorted to be corresponded to the intention classification of maximum probability, as the statement text to be sorted.
In conclusion it is provided in an embodiment of the present invention another kind intension recognizing method, can according to the input information of user, It determines statement text to be sorted, classification statement text is then treated by intention assessment model and is divided, table to be sorted is obtained State text it is corresponding at least one is text filed, it is then, corresponding every by intention assessment model extraction statement text to be sorted A text filed region vector, region phonetic vector and area label vector, finally, being based on by intention assessment model every A text filed region vector, region phonetic vector and area label vector determine the intention class of statement text to be sorted Not, in the embodiment of the present invention, using the intention assessment model when treating classification statement text progress intention assessment, can be based on should Region vector, region phonetic vector and the area label vector of statement text to be sorted are identified, due to region phonetic Vector, which can reduce in statement text to be sorted, there is word deviation caused by classification accuracy of identification mistake, passes through region Label vector can reduce to domanial words there are ambiguity cause classification results and user it is practical be intended to occur deviation, Jin Erke To improve the accuracy rate of intention assessment.
Fig. 5 is the step flow chart of another intension recognizing method provided in an embodiment of the present invention, as shown in figure 5, the party Method may include:
Step 501, each sample text for concentrating training sample input initial intention assessment model.
Specifically, the implementation of this step can refer to above-mentioned steps 101, the embodiment of the present invention is not construed as limiting this.
Step 502, according to sample areas vector, the sample in the corresponding each sample text region of each sample text Region phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, determine each sample The prediction intention value of text.
Specifically, the implementation of this step can refer to above-mentioned steps 102, the embodiment of the present invention is not construed as limiting this.
The true intention value of step 503, the prediction intention value based on each sample text and each sample text, Calculate the penalty values of the initial intention assessment model.
Specifically, the implementation of this step can refer to above-mentioned steps 103, the embodiment of the present invention is not construed as limiting this.
Step 504, when the penalty values within a preset range, using the initial intention assessment model as intention assessment mould Type.
Specifically, the implementation of this step can refer to above-mentioned steps 104, the embodiment of the present invention is not construed as limiting this.
Step 505, the input information according to user, determine statement text to be sorted.
Specifically, the implementation of this step can refer to above-mentioned steps 301, the embodiment of the present invention is not construed as limiting this.
Step 506, by the statement text input intention assessment model to be sorted, by the intention assessment model to institute It states statement text to be sorted and carries out intention assessment, obtain the intention classification of the statement text to be sorted.
Specifically, the implementation of this step can refer to above-mentioned steps 302, the embodiment of the present invention is not construed as limiting this.
In conclusion another intension recognizing method provided in an embodiment of the present invention, training sample can be concentrated every A sample text inputs initial intention assessment model, then, according to the corresponding each sample text region of each sample text Sample areas vector, sample areas phonetic vector and sample areas label vector, and initial intention assessment model is utilized, it determines The prediction intention value of each sample text, then the prediction intention value based on each sample text and each sample text is true Sincere map values calculate the penalty values of initial intention assessment model, when penalty values within a preset range, can be by initial intention assessment mould Type is as intention assessment model, then, according to the input information of user, determines statement text to be sorted, by statement text to be sorted This input intention assessment model carries out intention assessment to the statement text to be sorted by the intention assessment model, obtains The intention classification of statement text to be sorted.In the embodiment of the present invention, classification chart is being treated using the intention assessment model of the generation It, can be based on region vector, region phonetic vector and the region of the statement text to be sorted when stating text progress intention assessment Label vector is identified the word of identification mistake occur since region phonetic vector can reduce in statement text to be sorted The deviation caused by classification accuracy, can reduce that there are ambiguities to lead to classification results to domanial words by area label vector And the practical deviation for being intended to occur of user, and then the accuracy rate of intention assessment can be improved.
Fig. 6 is a kind of block diagram of intention assessment model generating means provided in an embodiment of the present invention, as shown in fig. 6, the dress Setting 60 may include:
Input module 601, each sample text for concentrating training sample input initial intention assessment model；
First determining module 602, for the sample according to the corresponding each sample text region of each sample text Region vector, sample areas phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, it determines The prediction intention value of each sample text；
Computing module 602, for based on each sample text prediction intention value and each sample text it is true Sincere map values calculate the penalty values of the initial intention assessment model；
Second determining module 604 makees the initial intention assessment model for working as the penalty values within a preset range For intention assessment model.
In conclusion a kind of intention assessment model generating means provided in an embodiment of the present invention, it can be by training sample set In each sample text input initial intention assessment model, then, according to the corresponding each sample text of each sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region, and utilize initial intention assessment mould Type determines the prediction intention value of each sample text, then the prediction intention value based on each sample text and each sample The true intention value of text calculates the penalty values of initial intention assessment model, when penalty values within a preset range, will can initially anticipate Figure identification model is as intention assessment model.In the embodiment of the present invention, by extracting the corresponding each sample text of sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region generates intention assessment model, makes It obtains in subsequent use process, using the intention assessment model of the generation when treating classification statement text progress intention assessment, energy It is enough to be identified based on region vector, region phonetic vector and the area label vector of the statement text to be sorted, due to Region phonetic vector, which can reduce in statement text to be sorted, there is word deviation caused by classification accuracy of identification mistake, By area label vector can reduce to domanial words there are ambiguity cause classification results and user it is practical be intended to occur it is inclined Difference, and then the accuracy rate of intention assessment can be improved.
Optionally, described device 60 further include:
Module is adjusted, for working as the penalty values not in the preset range, adjusts the initial intention assessment model Parameter, and initial intention assessment model adjusted is continued to train based on the training sample set.
Optionally, described device 60 further include:
Initialization module is initialized for the parameter to each layer in initial intention assessment model；Wherein, described initial Intention assessment model includes region vector aligned layer, region vector attention layer, full articulamentum and soft maximum value softmax Layer；
The parameter of the region vector aligned layer includes at least: the term vector of each sample word in each sample text, The context term vector matrix of phonetic term vector and context term vector matrix and each entity tag.
Optionally, first determining module 602, comprising:
First divides submodule, for for each sample text, which to be inputted the region vector Aligned layer divides the sample text based on the region vector aligned layer, obtains the sample text corresponding at least one A sample text region；
First extracting sub-module, for extracting the corresponding each sample of the sample text for each sample text Text filed sample areas vector, sample areas phonetic vector and sample areas label vector；
First determines submodule, for the sample based on the corresponding each sample text region of each sample text One's respective area vector, sample areas phonetic vector and sample areas label vector determine the prediction meaning of each sample text Map values.
Optionally, first extracting sub-module, comprising:
First computing unit, it is each in each sample text region for being based on by the region vector aligned layer The term vector of sample word calculates the sample areas vector in each sample text region；
Second computing unit, it is each in each sample text region for being based on by the region vector aligned layer The phonetic term vector of sample word calculates the sample areas phonetic vector in each sample text region；
Third computing unit belongs to for being determined in each sample text region by the region vector aligned layer The corresponding entity tag of sample word of target domain, and based on the sample for belonging to target domain in each sample text region The corresponding entity tag of this word calculates the sample areas label vector in each sample text region.
Optionally, first computing unit, is used for:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the term vector of the core word and the context term vector square of the core word as core word Battle array；
By the context term vector matrix and each sample in text filed is abutted by the region vector aligned layer The term vector of word carries out the first dot product operation, and carries out maximum pond to the result of first dot product operation, obtains the sample This text filed region vector.
Optionally, second computing unit, is used for:
For each sample text region, by the region vector aligned layer by the sample text region interposition The sample word set determines the phonetic term vector of the core word and the context pinyin word of the core word as core word Vector matrix；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each sample word carries out the second dot product operation, and carries out maximum pond to the result of second dot product operation Change, obtains the region phonetic vector in the sample text region.
Optionally, the third computing unit, is used for:
For each sample text region, determined by the region vector aligned layer every in the sample text region The context term vector matrix of a entity tag；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer The term vector of each sample word carries out the operation of third dot product in region, and carries out to the result of third dot product operation maximum Chi Hua obtains the corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector in the sample text region.
In conclusion a kind of intention assessment model generating means provided in an embodiment of the present invention, it can be by training sample set In each sample text input initial intention assessment model, then, according to the corresponding each sample text of each sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region, and utilize initial intention assessment mould Type determines the prediction intention value of each sample text, then the prediction intention value based on each sample text and each sample The true intention value of text calculates the penalty values of initial intention assessment model, when penalty values within a preset range, will can initially anticipate Figure identification model as intention assessment model, when penalty values not within a preset range, the ginseng of initial intention assessment model can be adjusted Number, continues to train.In the embodiment of the present invention, by extract the sample areas in the corresponding each sample text region of sample text to Amount, sample areas phonetic vector and sample areas label vector generate intention assessment model, so that in subsequent use process, Using the intention assessment model of the generation when treating classification statement text progress intention assessment, the statement to be sorted can be based on Region vector, region phonetic vector and the area label vector of text is identified, since region phonetic vector can drop There is word deviation caused by classification accuracy of identification mistake in low statement text to be sorted, it can by area label vector With reduction, to domanial words, there are ambiguities to lead to classification results and the practical deviation for being intended to occur of user, and then intention can be improved The accuracy rate of identification.
Fig. 7 is a kind of block diagram of intention assessment device provided in an embodiment of the present invention, as shown in fig. 7, the device 70 can be with Include:
Third determining module 701 determines statement text to be sorted for the input information according to user；
Identification module 702, for passing through the intention assessment for the statement text input intention assessment model to be sorted Model carries out intention assessment to the statement text to be sorted, obtains the intention classification of the statement text to be sorted；Wherein, institute Stating intention assessment model is generated using device described in Fig. 6.
In conclusion a kind of intention assessment device provided in an embodiment of the present invention, it can be according to the input information of user, really Fixed statement text to be sorted is treated point then by statement text input intention assessment model to be sorted by intention assessment model Class states text and carries out intention assessment, obtains the intention classification of statement text to be sorted, wherein the intention assessment model is to pass through Extract sample areas vector, sample areas phonetic vector and the sample areas in the corresponding each sample text region of sample text Label vector, and the generation of this these vector based on extraction, therefore, in the embodiment of the present invention, utilize the intention assessment model It, can be based on region vector, the region phonetic of the statement text to be sorted when treating classification statement text progress intention assessment Vector and area label vector are identified, are known since region phonetic vector can reduce in statement text to be sorted Not wrong word deviation caused by classification accuracy, can reduce that there are ambiguities to domanial words by area label vector Lead to classification results and the practical deviation for being intended to occur of user, and then the accuracy rate of intention assessment can be improved.
Optionally, the identification module 702, comprising:
Second divides submodule, for being divided by the intention assessment model to the statement text to be sorted, Obtaining the statement text to be sorted, corresponding at least one is text filed；
Second extracting sub-module, for corresponding every by statement text to be sorted described in the intention assessment model extraction A text filed region vector, region phonetic vector and area label vector；
Second determine submodule, for by the intention assessment model based on each text filed region to Amount, region phonetic vector and area label vector determine the intention classification of the statement text to be sorted.
Optionally, the intention assessment model includes region vector aligned layer；
Second extracting sub-module, comprising:
4th computing unit, for by the region vector aligned layer be based on it is each it is described it is text filed in each word Term vector, calculate each text filed region vector；
5th computing unit, for by the region vector aligned layer be based on it is each it is described it is text filed in each word Phonetic term vector, calculate each text filed region phonetic vector；
6th computing unit, for by the region vector aligned layer determine it is each it is described it is text filed in belong to target The corresponding entity tag of the domanial words in field, and based on it is each it is described it is text filed in the corresponding entity mark of each domanial words Label calculate each text filed area label vector.
Optionally, the intention assessment model further includes region vector attention layer, full articulamentum and softmax layers；
Described second determines submodule, is used for:
By the region vector aligned layer by each text filed region vector, region phonetic vector and area Domain label vector is spliced, and each text filed final area vector is obtained；
The weight of each text filed final area vector is determined by the region vector attention layer；
By the full articulamentum based on each text filed the final area vector and its weight, determine it is described to The class vector of classification statement text；
The intention classification of the statement text to be sorted is determined based on the class vector by described softmax layers.
Optionally, described second submodule is divided, is used for:
Participle operation is carried out to the statement text to be sorted by the region vector aligned layer, is obtained described to be sorted The m word that statement text includes；
I-th to the N+i word in the m word is divided into a text by the region vector aligned layer Region, obtaining the statement text to be sorted, corresponding at least one is text filed；
Wherein, the i=1...m-N, the N and the m are the positive integer greater than 1.
Optionally, the 4th computing unit, is used for:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the term vector of the core word and the context term vector matrix of the core word are determined；
By the context term vector matrix and each word in text filed is abutted by the region vector aligned layer Term vector carry out the operation of the 4th dot product, and maximum pond is carried out to the result of the 4th dot product operation, obtains this article local area The region vector in domain.
Optionally, the 5th computing unit, is used for:
For each described text filed, by the region vector aligned layer by the word in this article one's respective area middle position As core word, the phonetic term vector of the core word and the context pinyin word vector matrix of the core word are determined；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in The phonetic term vector of each word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, Obtain the region phonetic vector of this article one's respective area.
Optionally, the 6th computing unit, is used for:
For each described text filed, preset target domain dictionary is based on by the region vector aligned layer, it will The word for belonging to the target domain dictionary in the word that this article one's respective area includes is determined as domanial words；
The corresponding label of each domanial words is searched in the target domain dictionary by the region vector aligned layer, Obtain the entity tag of each domanial words.
Optionally, the 6th computing unit, is used for:
For each described text filed, each entity mark in this article one's respective area is determined by the region vector aligned layer The context term vector matrix of label；
By the context term vector matrix of each entity tag and text is abutted by the region vector aligned layer Each term vector carries out the operation of the 6th dot product in region, and carries out maximum pond to the result of the 6th dot product operation, obtains The corresponding maximum pond result of each entity tag；
The average value of the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, Obtain the area label term vector of this article one's respective area.
Optionally, the third determining module 701, is used for:
When the input information is voice messaging, the voice messaging is converted into text, and by the text and Formerly the corresponding text of input information is determined as statement text to be sorted；
Wherein, formerly input information is received in the preset time period before receiving the input information.
In conclusion a kind of intention assessment device provided in an embodiment of the present invention, it can be according to the input information of user, really Then fixed statement text to be sorted is treated classification statement text by intention assessment model and is divided, obtains statement to be sorted Text is corresponding, and at least one is text filed, then, corresponding each by intention assessment model extraction statement text to be sorted Text filed region vector, region phonetic vector and area label vector, finally, being based on by intention assessment model each Text filed region vector, region phonetic vector and area label vector determines the intention classification of statement text to be sorted, It, can be based on should be to using the intention assessment model when treating classification statement text and carrying out intention assessment in the embodiment of the present invention Region vector, region phonetic vector and the area label vector of classification statement text identified, due to region phonetic to Amount, which can reduce in statement text to be sorted, there is word deviation caused by classification accuracy of identification mistake, is marked by region Label vector can reduce that there are the deviations that ambiguity leads to classification results and the practical intention appearance of user to domanial words, and then can be with Improve the accuracy rate of intention assessment.
For above-mentioned apparatus embodiment, since it is basically similar to the method embodiment, so be described relatively simple, The relevent part can refer to the partial explaination of embodiments of method.
In addition, the embodiment of the present invention also provides a kind of terminal, including processor, memory, storage is on a memory and can The computer program run on the processor, the computer program realize above-mentioned intention assessment model when being executed by processor Generation method, each process of intension recognizing method embodiment, and identical technical effect can be reached, to avoid repeating, here It repeats no more.
The embodiment of the present invention also provides a kind of computer readable storage medium, and meter is stored on computer readable storage medium Calculation machine program, the computer program realize above-mentioned intention assessment model generating method, intension recognizing method when being executed by processor Each process of embodiment, and identical technical effect can be reached, to avoid repeating, which is not described herein again.Wherein, the meter Calculation machine readable storage medium storing program for executing, such as read-only memory (Read-Only Memory, abbreviation ROM), random access memory (Random Access Memory, abbreviation RAM), magnetic or disk etc..
All the embodiments in this specification are described in a progressive manner, the highlights of each of the examples are with The difference of other embodiments, the same or similar parts between the embodiments can be referred to each other.
It would have readily occurred to a person skilled in the art that: any combination application of above-mentioned each embodiment is all feasible, therefore Any combination between above-mentioned each embodiment is all embodiment of the present invention, but this specification exists as space is limited, This is not just detailed one by one.
Intention assessment model generating method, intension recognizing method are not with any certain computer, virtually provided herein System or other equipment are inherently related.Various general-purpose systems can also be used together with teachings based herein.According to above Description, it is obvious for constructing structure required by the system with the present invention program.In addition, the present invention is not also for any Certain programmed language.It should be understood that can use various programming languages realizes summary of the invention described herein, and above The description done to language-specific is in order to disclose the best mode of carrying out the invention.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail And technology, so as not to obscure the understanding of this specification.
It should be appreciated that in order to simplify the present invention and help to understand one or more of the various inventive aspects, it is right above In the description of exemplary embodiment of the present invention, each feature of the invention be grouped together into sometimes single embodiment, figure or In person's descriptions thereof.However, the disclosed method should not be interpreted as reflecting the following intention: i.e. claimed hair It is bright to require features more more than feature expressly recited in each claim.More precisely, such as claims institute As reflection, inventive aspect is all features less than single embodiment disclosed above.Therefore, it then follows specific embodiment party Thus claims of formula are expressly incorporated in the specific embodiment, wherein each claim itself is as of the invention Separate embodiments.
Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or subgroups Part.Other than such feature and/or at least some of process or unit exclude each other, any combination can be used To all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed any side All process or units of method or equipment are combined.Unless expressly stated otherwise, this specification (is wanted including adjoint right Ask, make a summary and attached drawing) disclosed in each feature can be replaced with an alternative feature that provides the same, equivalent, or similar purpose.
It will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments is wrapped Certain features for including rather than other feature, but the combination of the feature of different embodiments mean in the scope of the present invention it It is interior and form different embodiments.For example, in detail in the claims, the one of any of embodiment claimed is ok In any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors Software module realize, or with they combine realize.It should be appreciated that microprocessor or number letter can be used in practice Number processor (DSP) come realize intention assessment model generating method according to an embodiment of the present invention, one in intension recognizing method The some or all functions of a little or whole components.The present invention is also implemented as executing method as described herein Some or all device or device programs (for example, computer program and computer program product).Such realization Program of the invention can store on a computer-readable medium, or may be in the form of one or more signals.This The signal of sample can be downloaded from an internet website to obtain, and is perhaps provided on the carrier signal or mentions in any other forms For.
The above-mentioned embodiments illustrate rather than limit the invention, and those skilled in the art exist It can be designed alternative embodiment without departing from the scope of the appended claims.In the claims, it should not will be located at and include Any reference symbol between number is configured to limitations on claims.Word "comprising", which does not exclude the presence of, is not listed in claim In element or step.Word "a" or "an" located in front of the element does not exclude the presence of multiple such elements.The present invention It can be realized by means of including the hardware of several different elements and by means of properly programmed computer.If listing In the unit claim of equipment for drying, several in these devices, which can be, to be embodied by the same item of hardware.It is single The use of word first, second, and third does not indicate any sequence.These words can be construed to title.

Claims (36)

1. a kind of intention assessment model generating method, which is characterized in that the described method includes:
Each sample text that training sample is concentrated inputs initial intention assessment model；
According to sample areas vector, the sample areas phonetic vector in the corresponding each sample text region of each sample text And sample areas label vector, and the initial intention assessment model is utilized, determine the prediction meaning of each sample text Map values；
The true intention value of prediction intention value and each sample text based on each sample text calculates described initial The penalty values of intention assessment model；
When the penalty values within a preset range, using the initial intention assessment model as intention assessment model.
2. the method according to claim 1, wherein the penalty values for calculating the initial intention assessment model Later, the method also includes:
When the penalty values are not in the preset range, the parameter of the initial intention assessment model is adjusted, and based on described Training sample set continues to train to initial intention assessment model adjusted.
3. the method according to claim 1, wherein each sample text that training sample is concentrated inputs Before initial intention assessment model, the method also includes:
The parameter of each layer in initial intention assessment model is initialized；Wherein, the initial intention assessment model includes area Domain vector aligned layer, region vector attention layer, full articulamentum and maximum value softmax layers soft；
The parameter of the region vector aligned layer includes at least: term vector, the phonetic of each sample word in each sample text The context term vector matrix of term vector and context term vector matrix and each entity tag.
4. according to the method described in claim 3, it is characterized in that, described according to the corresponding each sample of each sample text This text filed sample areas vector, sample areas phonetic vector and sample areas label vector, and utilize described initial Intention assessment model determines the prediction intention value of each sample text, comprising:
For each sample text, which is inputted into the region vector aligned layer, is based on the region vector Aligned layer divides the sample text, obtains at least one corresponding sample text region of the sample text；
For each sample text, extract the corresponding each sample text region of the sample text sample areas vector, Sample areas phonetic vector and sample areas label vector；
Sample areas vector, sample areas phonetic based on the corresponding each sample text region of each sample text Vector and sample areas label vector determine the prediction intention value of each sample text.
5. according to the method described in claim 4, extracting the sample it is characterized in that, described for each sample text Sample areas vector, sample areas phonetic vector and the sample areas label in the corresponding each sample text region of text to Amount, comprising:
Term vector by the region vector aligned layer based on each sample word in each sample text region calculates The sample areas vector in each sample text region；
Phonetic term vector by the region vector aligned layer based on each sample word in each sample text region, Calculate the sample areas phonetic vector in each sample text region；
The sample word pair for belonging to target domain in each sample text region is determined by the region vector aligned layer The entity tag answered, and based on the corresponding entity mark of sample word for belonging to target domain in each sample text region Label calculate the sample areas label vector in each sample text region.
6. according to the method described in claim 5, it is characterized in that, described be based on each institute by the region vector aligned layer The term vector of each sample word in sample text region is stated, the sample areas vector in each sample text region is calculated, Include:
For each sample text region, by the region vector aligned layer by the sample text region middle position Sample word determines the term vector of the core word and the context term vector matrix of the core word as core word；
By the context term vector matrix and each sample word in text filed is abutted by the region vector aligned layer Term vector carry out the first dot product operation, and maximum pond is carried out to the result of first dot product operation, it is literary to obtain the sample The region vector of one's respective area.
7. according to the method described in claim 5, it is characterized in that, described be based on each institute by the region vector aligned layer The phonetic term vector of each sample word in sample text region is stated, the sample areas for calculating each sample text region is spelled Sound vector, comprising:
For each sample text region, by the region vector aligned layer by the sample text region middle position Sample word determines the phonetic term vector of the core word and the context phonetic term vector of the core word as core word Matrix；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in it is each The phonetic term vector of sample word carries out the second dot product operation, and carries out maximum pond to the result of second dot product operation, Obtain the region phonetic vector in the sample text region.
8. according to the method described in claim 5, it is characterized in that, described be based on belonging to mesh in each sample text region The corresponding entity tag of sample word in mark field calculates the sample areas label vector in each sample text region, packet It includes:
For each sample text region, each reality in the sample text region is determined by the region vector aligned layer The context term vector matrix of body label；
It is by the region vector aligned layer that the context term vector matrix of each entity tag and adjoining is text filed In the term vector of each sample word carry out the operation of third dot product, and maximum pond is carried out to the result of third dot product operation Change, obtains the corresponding maximum pond result of each entity tag；
The average value that the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, obtains The area label term vector in the sample text region.
9. a kind of intension recognizing method, which is characterized in that the described method includes:
According to the input information of user, statement text to be sorted is determined；
By the statement text input intention assessment model to be sorted, by the intention assessment model to the statement to be sorted Text carries out intention assessment, obtains the intention classification of the statement text to be sorted；Wherein, the intention assessment model is to utilize What method described in any item of the claim 1 to 8 generated.
10. according to the method described in claim 9, it is characterized in that, it is described by the intention assessment model to it is described to point Class states text and carries out intention assessment, obtains the intention classification of the statement text to be sorted, comprising:
The statement text to be sorted is divided by the intention assessment model, obtains the statement text pair to be sorted At least one answered is text filed；
Pass through the corresponding each text filed region vector of statement to be sorted text, area described in the intention assessment model extraction Domain phonetic vector and area label vector；
It is marked by the intention assessment model based on each text filed region vector, region phonetic vector and region Vector is signed, determines the intention classification of the statement text to be sorted.
11. according to the method described in claim 10, it is characterized in that, the intention assessment model includes the alignment of region vector Layer；
It is described by the corresponding each text filed region of statement to be sorted text described in the intention assessment model extraction to Amount, region phonetic vector and area label vector, comprising:
By the region vector aligned layer based on it is each it is described it is text filed in each word term vector, calculate each described Text filed region vector；
By the region vector aligned layer based on it is each it is described it is text filed in each word phonetic term vector, calculate each The text filed region phonetic vector；
By the region vector aligned layer determine it is each it is described it is text filed in belong to target domain domanial words it is corresponding Entity tag, and based on it is each it is described it is text filed in the corresponding entity tag of each domanial words, calculate each text The area label vector in region.
12. according to the method for claim 11, which is characterized in that the intention assessment model further includes that region vector pays attention to Power layer, full articulamentum and softmax layers；
It is described by the intention assessment model based on each text filed region vector, region phonetic vector and area Domain label vector determines the intention classification of the statement text to be sorted, comprising:
Each text filed region vector, region phonetic vector and region are marked by the region vector aligned layer Label vector is spliced, and each text filed final area vector is obtained；
The weight of each text filed final area vector is determined by the region vector attention layer；
By the full articulamentum based on each text filed the final area vector and its weight, determine described to be sorted State the class vector of text；
The intention classification of the statement text to be sorted is determined based on the class vector by described softmax layers.
13. according to the method for claim 11, which is characterized in that it is described by the intention assessment model to it is described to point Class statement text is divided, and obtaining the statement text to be sorted, corresponding at least one is text filed, comprising:
Participle operation is carried out to the statement text to be sorted by the region vector aligned layer, obtains the statement to be sorted The m word that text includes；
I-th to the N+i word in the m word is divided into a text area by the region vector aligned layer Domain, obtaining the statement text to be sorted, corresponding at least one is text filed；
Wherein, the i=1...m-N, the N and the m are the positive integer greater than 1.
14. according to the method for claim 11, which is characterized in that described to be based on each by the region vector aligned layer It is described it is text filed in each word term vector, calculate each text filed region vector, comprising:
For each described text filed, by the region vector aligned layer using the word in this article one's respective area middle position as Core word determines the term vector of the core word and the context term vector matrix of the core word；
By the region vector aligned layer by the context term vector matrix and it is adjacent it is text filed in each word word Vector carries out the operation of the 4th dot product, and carries out maximum pond to the result of the 4th dot product operation, obtains this article one's respective area Region vector.
15. according to the method for claim 11, which is characterized in that described to be based on each by the region vector aligned layer It is described it is text filed in each word phonetic term vector, calculate each text filed region phonetic vector, comprising:
For each described text filed, by the region vector aligned layer using the word in this article one's respective area middle position as Core word determines the phonetic term vector of the core word and the context pinyin word vector matrix of the core word；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in it is each The phonetic term vector of word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, obtains The region phonetic vector of this article one's respective area.
16. according to the method for claim 11, which is characterized in that described to be determined each by the region vector aligned layer It is described it is text filed in belong to the corresponding entity tag of domanial words of target domain, comprising:
For each described text filed, preset target domain dictionary is based on by the region vector aligned layer, by this article The word for belonging to the target domain dictionary in the word that one's respective area includes is determined as domanial words；
The corresponding label of each domanial words is searched in the target domain dictionary by the region vector aligned layer, is obtained The entity tag of each domanial words.
17. according to the method for claim 11, which is characterized in that it is described based on it is each it is described it is text filed in each field The corresponding entity tag of word calculates each text filed area label vector, comprising:
For each described text filed, each entity tag in this article one's respective area is determined by the region vector aligned layer Context term vector matrix；
It is by the region vector aligned layer that the context term vector matrix of each entity tag and adjoining is text filed In each term vector carry out the operation of the 6th dot product, and maximum pond is carried out to the result of the 6th dot product operation, obtained each The corresponding maximum pond result of the entity tag；
The average value that the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, obtains The area label term vector of this article one's respective area.
18. according to the method described in claim 9, it is characterized in that, the input information according to user, determines table to be sorted State text, comprising:
When the input information is voice messaging, the voice messaging is converted into text, and by the text and formerly The corresponding text of input information is determined as statement text to be sorted；
Wherein, formerly input information is received in the preset time period before receiving the input information.
19. a kind of intention assessment model generating means, which is characterized in that described device includes:
Input module, each sample text for concentrating training sample input initial intention assessment model；
First determining module, for according to the sample areas in the corresponding each sample text region of each sample text to Amount, sample areas phonetic vector and sample areas label vector, and the initial intention assessment model is utilized, determine each institute State the prediction intention value of sample text；
Computing module, the true intention for prediction intention value and each sample text based on each sample text Value calculates the penalty values of the initial intention assessment model；
Second determining module, for working as the penalty values within a preset range, using the initial intention assessment model as intention Identification model.
20. device according to claim 19, which is characterized in that described device further include:
Module is adjusted, for working as the penalty values not in the preset range, adjusts the ginseng of the initial intention assessment model Number, and initial intention assessment model adjusted is continued to train based on the training sample set.
21. device according to claim 19, which is characterized in that described device further include:
Initialization module is initialized for the parameter to each layer in initial intention assessment model；Wherein, the initial intention Identification model includes region vector aligned layer, region vector attention layer, full articulamentum and maximum value softmax layers soft；
The parameter of the region vector aligned layer includes at least: term vector, the phonetic of each sample word in each sample text The context term vector matrix of term vector and context term vector matrix and each entity tag.
22. device according to claim 21, which is characterized in that first determining module, comprising:
First divides submodule, for which being inputted the region vector and is aligned for each sample text Layer, divides the sample text based on the region vector aligned layer, obtains at least one corresponding sample of the sample text This is text filed；
First extracting sub-module, for extracting the corresponding each sample text of the sample text for each sample text Sample areas vector, sample areas phonetic vector and the sample areas label vector in region；
First determines submodule, for the sample area based on the corresponding each sample text region of each sample text Domain vector, sample areas phonetic vector and sample areas label vector determine the prediction intention value of each sample text.
23. device according to claim 22, which is characterized in that first extracting sub-module, comprising:
First computing unit, for being based on each sample in each sample text region by the region vector aligned layer The term vector of word calculates the sample areas vector in each sample text region；
Second computing unit, for being based on each sample in each sample text region by the region vector aligned layer The phonetic term vector of word calculates the sample areas phonetic vector in each sample text region；
Third computing unit belongs to target for determining in each sample text region by the region vector aligned layer The corresponding entity tag of sample word in field, and based on the sample word for belonging to target domain in each sample text region The corresponding entity tag of language calculates the sample areas label vector in each sample text region.
24. device according to claim 23, which is characterized in that first computing unit is used for:
For each sample text region, by the region vector aligned layer by the sample text region middle position Sample word determines the term vector of the core word and the context term vector matrix of the core word as core word；
By the context term vector matrix and each sample word in text filed is abutted by the region vector aligned layer Term vector carry out the first dot product operation, and maximum pond is carried out to the result of first dot product operation, it is literary to obtain the sample The region vector of one's respective area.
25. device according to claim 23, which is characterized in that second computing unit is used for:
For each sample text region, by the region vector aligned layer by the sample text region middle position Sample word determines the phonetic term vector of the core word and the context phonetic term vector of the core word as core word Matrix；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in it is each The phonetic term vector of sample word carries out the second dot product operation, and carries out maximum pond to the result of second dot product operation, Obtain the region phonetic vector in the sample text region.
26. device according to claim 23, which is characterized in that the third computing unit is used for:
For each sample text region, each reality in the sample text region is determined by the region vector aligned layer The context term vector matrix of body label；
It is by the region vector aligned layer that the context term vector matrix of each entity tag and adjoining is text filed In the term vector of each sample word carry out the operation of third dot product, and maximum pond is carried out to the result of third dot product operation Change, obtains the corresponding maximum pond result of each entity tag；
The average value that the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, obtains The area label term vector in the sample text region.
27. a kind of intention assessment device, which is characterized in that described device includes:
Third determining module determines statement text to be sorted for the input information according to user；
Identification module, for passing through the intention assessment model pair for the statement text input intention assessment model to be sorted The statement text to be sorted carries out intention assessment, obtains the intention classification of the statement text to be sorted；Wherein, the intention Identification model is generated using device described in any one of claim 19 to 26.
28. device according to claim 27, which is characterized in that the identification module, comprising:
Second division submodule is obtained for being divided by the intention assessment model to the statement text to be sorted The statement text to be sorted is corresponding, and at least one is text filed；
Second extracting sub-module, for passing through the corresponding each text of statement text to be sorted described in the intention assessment model extraction Region vector, region phonetic vector and the area label vector of one's respective area；
Second determines submodule, for passing through the intention assessment model based on each text filed region vector, area Domain phonetic vector and area label vector determine the intention classification of the statement text to be sorted.
29. device according to claim 28, which is characterized in that the intention assessment model includes the alignment of region vector Layer；
Second extracting sub-module, comprising:
4th computing unit, for by the region vector aligned layer based on it is each it is described it is text filed in each word word Vector calculates each text filed region vector；
5th computing unit, for by the region vector aligned layer based on it is each it is described it is text filed in each word spelling Sound term vector calculates each text filed region phonetic vector；
6th computing unit, for by the region vector aligned layer determine it is each it is described it is text filed in belong to target domain The corresponding entity tag of domanial words, and based on it is each it is described it is text filed in the corresponding entity tag of each domanial words, Calculate each text filed area label vector.
30. device according to claim 29, which is characterized in that the intention assessment model further includes that region vector pays attention to Power layer, full articulamentum and softmax layers；
Described second determines submodule, is used for:
Each text filed region vector, region phonetic vector and region are marked by the region vector aligned layer Label vector is spliced, and each text filed final area vector is obtained；
The weight of each text filed final area vector is determined by the region vector attention layer；
By the full articulamentum based on each text filed the final area vector and its weight, determine described to be sorted State the class vector of text；
The intention classification of the statement text to be sorted is determined based on the class vector by described softmax layers.
31. device according to claim 29, which is characterized in that described second divides submodule, is used for:
Participle operation is carried out to the statement text to be sorted by the region vector aligned layer, obtains the statement to be sorted The m word that text includes；
I-th to the N+i word in the m word is divided into a text area by the region vector aligned layer Domain, obtaining the statement text to be sorted, corresponding at least one is text filed；
Wherein, the i=1...m-N, the N and the m are the positive integer greater than 1.
32. device according to claim 29, which is characterized in that the 4th computing unit is used for:
For each described text filed, by the region vector aligned layer using the word in this article one's respective area middle position as Core word determines the term vector of the core word and the context term vector matrix of the core word；
By the region vector aligned layer by the context term vector matrix and it is adjacent it is text filed in each word word Vector carries out the operation of the 4th dot product, and carries out maximum pond to the result of the 4th dot product operation, obtains this article one's respective area Region vector.
33. device according to claim 29, which is characterized in that the 5th computing unit is used for:
For each described text filed, by the region vector aligned layer using the word in this article one's respective area middle position as Core word determines the phonetic term vector of the core word and the context pinyin word vector matrix of the core word；
By the region vector aligned layer by the context pinyin word vector matrix and the adjoining it is text filed in it is each The phonetic term vector of word carries out the operation of the 5th dot product, and carries out maximum pond to the result of the 5th dot product operation, obtains The region phonetic vector of this article one's respective area.
34. device according to claim 29, which is characterized in that the 6th computing unit is used for:
For each described text filed, preset target domain dictionary is based on by the region vector aligned layer, by this article The word for belonging to the target domain dictionary in the word that one's respective area includes is determined as domanial words；
The corresponding label of each domanial words is searched in the target domain dictionary by the region vector aligned layer, is obtained The entity tag of each domanial words.
35. device according to claim 29, which is characterized in that the 6th computing unit is used for:
For each described text filed, each entity tag in this article one's respective area is determined by the region vector aligned layer Context term vector matrix；
It is by the region vector aligned layer that the context term vector matrix of each entity tag and adjoining is text filed In each term vector carry out the operation of the 6th dot product, and maximum pond is carried out to the result of the 6th dot product operation, obtained each The corresponding maximum pond result of the entity tag；
The average value that the corresponding maximum pond result of each entity tag is calculated by the region vector aligned layer, obtains The area label term vector of this article one's respective area.
36. device according to claim 27, which is characterized in that the third determining module is used for:
When the input information is voice messaging, the voice messaging is converted into text, and by the text and formerly The corresponding text of input information is determined as statement text to be sorted；
Wherein, formerly input information is received in the preset time period before receiving the input information.
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