CN112131391A - Power supply service client appeal text classification method based on capsule network - Google Patents

Power supply service client appeal text classification method based on capsule network Download PDF

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CN112131391A
CN112131391A CN202011332961.4A CN202011332961A CN112131391A CN 112131391 A CN112131391 A CN 112131391A CN 202011332961 A CN202011332961 A CN 202011332961A CN 112131391 A CN112131391 A CN 112131391A
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杨志新
周宇
王成现
潘留兴
洪昕
丁淙
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Jiangsu Electric Power Information Technology Co Ltd
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Abstract

The invention discloses a power supply service client appeal text classification method based on a capsule network, which comprises the following steps of: preprocessing a power supply service client appeal text; generating a power supply service client appeal text word vector to solve the problem of word ambiguity; acquiring a dependency relationship between words based on the encoding and dependency relationship acquisition of the word vector; obtaining a fusion vector based on the appeal text feature fusion of the characters and the word vectors; obtaining the interdependence relation between word vectors by adopting a self-attention mechanism; adopting an EM-Routing dynamic Routing algorithm to aggregate appeal text capsules to obtain semantic information relation among words; and finally, calculating loss values of the positive class and the negative class of the appeal text by adopting an interval loss function. The text classification method and the device improve the text important feature extraction capability and finally improve the accuracy of text classification of power supply appeal in the power customer service worksheet.

Description

Power supply service client appeal text classification method based on capsule network
Technical Field
The invention relates to the technical field of electric power, in particular to power supply service client appeal text classification based on a capsule network.
Background
The power supply service quality is an important index for measuring the service level of the power enterprise, and along with the rapid development of the internet, big data and a social platform, the improvement of the power supply service quality is imperative. 95598 as a service hot line of an electric power enterprise, communication and exchange with an electric power client are performed every day through the hot line, and a large amount of client appeal text data is formed. The best method for improving 95598 customer service capability is to clearly understand customer service requirements and problems represented in the customer appeal texts, so that accurate, active and differentiated power supply service is provided, customer power utilization satisfaction is improved, good image and brand value of power supply enterprises are improved, and stability of power utilization customers is guaranteed. However, the power work order client in the power supply service appeals that the text data has the characteristics of large information amount and unstructured data, and challenges are brought to development of refined power supply services.
In the traditional text classification based on the Convolutional Neural Network (CNN), when modeling is performed on spatial text information, too many training model parameters are generated, the model training efficiency is reduced, a lot of valuable text feature information is lost in a pooling layer, the feature understanding capability is not strong, and word position information, grammatical structures and semantic information in the text cannot be fully expressed due to the invariance of the spatial position of the CNN. Text classification based on Capsule networks (CapsNets) is a relatively new topic at present, and the defect of CNN model training is effectively improved. The capsule network can give consideration to the semantic and word order information of the text globally, improves the feature expression capability of each word in the text, and is very suitable for analyzing the unstructured power supply service client appeal text. Compared with a Long Short-Term Memory (LSTM) network, the method can break through the limitation of text distance (such as Long text) to obtain the semantic and word order information of the context. Compared with the traditional self-attention mechanism and CNN pooling operation, the capsule network adopts an Expectation-Maximization (EM-Routing) algorithm to overcome the defect of information aggregation in a bottom-up and passive mode, guide active clustering of task text information, mine the word order and semantic information of the power supply service client appeal text, and improve the classification accuracy of the client appeal text.
In summary, because the content of the power supply service client appeal is mainly in a text form, unstructured and large in information amount, and there is a word ambiguity, the method for classifying the power supply service client appeal text based on the traditional neural network has at least the following 3 problems: (1) the CNN pooling layer loses a lot of valuable information and passively performs information aggregation, so that the feature understanding capability is reduced; (2) the CNN has weak spatial relationship recognition capability, and cannot fully express word order, semantic information and grammatical structure in the power supply service appeal text; (3) LSTM cannot obtain contextual semantic and lexical information for long text solicited by power service customers.
Disclosure of Invention
The invention aims to provide a power supply service client appeal text classification method based on a capsule network, which improves the text important feature extraction capability and improves the accuracy of power supply appeal text classification in a 95598 power supply client service worksheet, and aims to overcome the defects that the feature expression capability of CNN and LSTM networks is not strong, the limitation of text distance cannot be broken through, and context semantics and language sequence information can be obtained, and the power supply service client appeal text data is unstructured, large in information quantity and ambiguous.
The invention is realized by the following technical scheme:
1) power supply service customer appeal text preprocessing: the method mainly comprises the steps of obtaining power supply service client appeal texts, removing stop words and performing word segmentation processing, wherein client appeal contents in a 95598 system are divided into reporting, suggesting, showing, complaint, inquiry, repair, past business and reflecting categories according to text forms, one line of texts represents one work order appeal and is added with text category labels, all symbols and stop words except letters, numbers and Chinese characters are deleted, and a jieba word segmentation tool is used for performing word segmentation on the work order appeal texts of each line.
2) Generating a text word vector by a power supply service client appeal: each line of power supply service client appeal text after preprocessing
Figure 471796DEST_PATH_IMAGE001
Inputting the result into a bert pre-training model, solving the problem of word ambiguity, and obtaining a dynamic word vector based on semantics
Figure 621017DEST_PATH_IMAGE002
Is shown asiThe dimension corresponding to each word isD 1The vector of (a) is determined,w ithe first in the text of each line of power supply client appealiThe number of the individual words is,L 1representing the number of words in each line of the power service client appeal text.
3) And obtaining the coding and dependency relationship based on the word vector: the word vector-based encoding is different from the traditional word vector-based encoding, the finer-grained encoding is realized, and the word set in the text of each line of power supply service customer appeal is assumed
Figure 829145DEST_PATH_IMAGE003
Wherein
Figure 548970DEST_PATH_IMAGE004
The first in the text of each line of work orderiWords, mapping the words to a high-dimensional space to obtain a word vector
Figure 235166DEST_PATH_IMAGE005
Wherein
Figure 238894DEST_PATH_IMAGE006
Is shown asiThe dimension corresponding to each word isD 2The vector of words of (a) is,L 2and the number of words in the appeal text of each line of the power supply service client is represented.
In the sentence, because each word is relatively independent, the mutual dependency relationship among the words in the power supply client appeal text is obtained by adopting N-garm convolution operation to generate phrase characteristics, and then important semantic characteristics are obtained through maximum pooling operation and are spliced to obtain the word-based power supply service client appeal text characteristic vector.
4) Text feature fusion based on word and word vectors: fusing the text characteristic vectors of the power supply service client appeal based on characters and words to obtain a fusion vector
Figure 352344DEST_PATH_IMAGE007
Is shown asiThe corresponding logical vector representation of the individual words.
5) Obtaining the correlation among the word vectors: and acquiring the interrelationship between the text words of each power supply service client appeal by adopting a self-attention mechanism.
6) Power supply client appeal text capsule aggregation: and aggregating the word capsules into power supply service client appeal text capsules by adopting an EM-Routing dynamic Routing algorithm to obtain semantic information relation among the words.
7) Calculating a loss value: the distance between the positive class and the negative class of each line of power supply service client appeal text is calculated by adopting an interval loss functionmLoss value ofL iAnd adding the loss values of the positive class and the negative class to obtain a total loss valueL. The smaller the loss function L is, the higher the classification accuracy is, and finally the classification accuracy of the appeal text of the power supply service client is improved.
The advantages of the method are as follows: the method overcomes the defect of difficulty in obtaining long text semantics, expresses global semantics and word order characteristics of the appeal text in a finer granularity by coding based on word vectors, aggregates each word into a capsule, further obtains semantic information relation between the words, improves text important characteristic extraction capability, and finally improves accuracy of appeal text classification in a customer service work order.
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Fig. 1 is a frame diagram of a power supply service client appeal text classification method based on a capsule network according to the present invention.
FIG. 2 is a specific process of text classification implemented by the matrix capsule network according to the present invention.
Detailed Description
The method of the present invention is further described with reference to the accompanying drawings and the detailed description.
Fig. 1 is a framework diagram of a power supply service client appeal text classification method based on a capsule network according to the present invention, and the method includes: (1) preprocessing a large amount of power supply service client appeal texts; (2) training to generate a client appeal text word vector; (3) encoding the word vectors and obtaining the interdependence relation between the words and the word vectors; (4) text feature fusion based on the word and word vector; (5) obtaining the interdependence relation between words and word vectors; (6) aggregating the word capsules into text capsules using an EM-Routing algorithm; (7) the loss values for the positive and negative classes are calculated. And finally, the classification accuracy of the power supply client appeal text is improved. FIG. 2 is a specific process of text classification implemented by the matrix capsule network according to the present invention. The specific implementation mode is as follows:
step 1, preprocessing power supply service client appeal text
All the client appeal contents of 95598 are divided into reports, suggestions, prawns, complaints, queries, repair reports, past services and reflection categories according to text forms, one line of text represents one power supply client appeal text and is added with a text category label, and a jieba word segmentation tool is used for segmenting the power supply client appeal text of each line.
Step 2, generating text word vectors for power supply service customer appeal
Appeal text of each line of preprocessed work order
Figure 808733DEST_PATH_IMAGE008
Inputting the dynamic word vector into a bert pre-training model to obtain a dynamic word vector based on semantics
Figure 49353DEST_PATH_IMAGE009
And then:
Figure 642008DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 457517DEST_PATH_IMAGE011
is shown asiThe dimension corresponding to each word isD 1The vector of (a) is determined,w ithe first in the text of each line of power supply client appealiThe number of the individual words is,L 1and the number of words in the appeal text of each line of the power supply client is represented.
Step 3, obtaining coding and dependency relation based on word vector
Different from the traditional encoding based on word vectors, the encoding with finer granularity is realized, and the mutual dependency relationship among the words in the power supply client appeal text is obtained by adopting the N-garm convolution operation.
3.1 word vector based coding: set of words in per-line power-supply client appeal text
Figure 135623DEST_PATH_IMAGE012
Wherein
Figure 163622DEST_PATH_IMAGE013
The first in the text of each line of power supply client appealiWords, mapping the words to a high-dimensional space to obtain a word vector
Figure 876363DEST_PATH_IMAGE014
Wherein
Figure 610577DEST_PATH_IMAGE015
Is shown asiThe dimension corresponding to each word isD 2The vector of words of (a) is,L 2and the number of the words in the appeal text of each line of the power supply client is represented.
3.2 word-based text feature vector acquisition
3.2.1 generating phrase features: in the sentence, because each word is relatively independent, the power supply client appeal text is obtained by adopting an N-garm convolution operation firstlyThe mutual dependency relationship between the Chinese characters is the local characteristics of the client appeal text, and the phrase characteristics are generated. Suppose that
Figure 41558DEST_PATH_IMAGE016
Representing word vectors
Figure 873248DEST_PATH_IMAGE017
The process of the volume of the words is as follows:
Figure 174916DEST_PATH_IMAGE018
wherein
Figure 332228DEST_PATH_IMAGE019
，kWhich represents the size of the convolution kernel and,b 1a bias term is represented as a function of,
Figure 735658DEST_PATH_IMAGE020
the generated local phrase features are represented as,D 3indicates the number of phrases to be generated,frepresenting a ReLU nonlinear activation function, adopting convolution operation at different positions of a sentence, and generating a phrase with the characteristics of
Figure 636618DEST_PATH_IMAGE021
。
3.2.2 obtaining semantic features of phrase features: obtaining important semantic features through maximum pooling operation, wherein the pooling process comprises the following steps:
Figure 323952DEST_PATH_IMAGE022
wherein
Figure 386586DEST_PATH_IMAGE023
Is shown asiAnd obtaining a characteristic value of the characteristic extracted by the convolution kernel through a maximum pooling operation.
3.2.3 eigenvalue stitching: splicing the obtained characteristic values to obtain an appeal text characteristic vector generated based on the word vector
Figure 277312DEST_PATH_IMAGE024
WhereinD 3The number of the characteristic values is represented,
Figure 716384DEST_PATH_IMAGE025
a text feature vector representing a word-based power client appeal.
Step 4, appeal text feature fusion based on characters and word vectors
Fusing the power supply client appeal text characteristic vectors based on characters and words to obtain a fusion vector
Figure 258224DEST_PATH_IMAGE026
Is shown asiA logical vector representation corresponding to a word, then
Figure 491759DEST_PATH_IMAGE027
。
Step 5, obtaining the interdependence relation between the word vectors
And obtaining the interdependence relation between each power supply client appeal text word by adopting a self-attention mechanism.
5.1 obtaining semantic vectors between words
Figure 119049DEST_PATH_IMAGE028
: first, calculate theiA word and ajSemantic information vector between words
Figure 830653DEST_PATH_IMAGE029
Wherein
Figure 977732DEST_PATH_IMAGE030
The information is represented by a transformation matrix of information,
Figure 647748DEST_PATH_IMAGE031
representing a fused vector
Figure 496755DEST_PATH_IMAGE032
Is/are as followsQA vector of values is generated by a vector of values,
Figure 12050DEST_PATH_IMAGE033
representing a fused vectorKA vector of values.
5.2 normalization calculation: computing collaborative weights for semantic information vectors by normalization operations
Figure 528482DEST_PATH_IMAGE034
Fused vector
Figure 103820DEST_PATH_IMAGE035
Is shown asjWord vectorhTo obtain a vector of values of
Figure 193785DEST_PATH_IMAGE036
。
Step 6, the power supply service client appeal text capsule aggregation
And adopting an EM-Routing dynamic Routing algorithm to aggregate the word capsules into power supply client appeal text capsules to obtain the semantic information relationship among the words.
6.1 calculating the probability Density
Figure 883710DEST_PATH_IMAGE038
: calculating high-level capsule asjIn time, the bottom layer capsuleiIn the first placehProbability density in one dimension
Figure 629949DEST_PATH_IMAGE039
To represent
Figure 453548DEST_PATH_IMAGE028
In thathThe value in the dimension(s) is,
Figure 576225DEST_PATH_IMAGE040
indicating high-level capsulesjIn thathMean value in dimension
Figure 286823DEST_PATH_IMAGE041
Indicating high-level capsulesjIn thathThe variance σ in the dimension;
6.2 logarithm of probability Density
Figure 469543DEST_PATH_IMAGE042
；
6.3 encapsulating each of the bottom layersiIn thathVector value in dimension to high level capsulejVector value summation QUOTE
Figure 780439DEST_PATH_IMAGE043
Figure 441227DEST_PATH_IMAGE043
：
Figure 521179DEST_PATH_IMAGE044
Wherein
Figure DEST_PATH_IMAGE045
Indicating allocation to higher level capsulesjThe total amount of data of (c);
6.4 calculate high level CapsulejActivation value of
Figure 891111DEST_PATH_IMAGE046
，
Figure 689303DEST_PATH_IMAGE047
Also indicating a high level capsulejThe probability of the presence of the object,
Figure 888203DEST_PATH_IMAGE048
are all hyper-parameters.
Step 7, calculating loss value
The distance between the positive class and the negative class of each line of power supply client appeal text is calculated by adopting an interval loss functionmLoss value of
Figure 822661DEST_PATH_IMAGE049
The positive class is represented by a positive number,
Figure 878342DEST_PATH_IMAGE050
representing negative classes, and then adding the loss values to obtain a total loss value
Figure 648983DEST_PATH_IMAGE051
。

Claims (9)

1. A power supply service client appeal text classification method based on a capsule network is characterized by comprising the following steps:
1) power supply service customer appeal text preprocessing: firstly, acquiring an appeal text, adding a text category label, deleting the stop and performing word segmentation;
2) generating a text word vector by a power supply service client appeal: inputting each line of the preprocessed appealing text into a bert pre-training model to solve the problem of word ambiguity;
3) and obtaining the coding and dependency relationship based on the word vector: adopting a word-based finer-grained appeal text coding mode and adopting N-garm convolution operation to obtain the interdependence relation between words in the appeal text;
4) appeal text feature fusion based on word and word vectors: fusing appeal text characteristic vectors based on characters and words to obtain a fused vector;
5) obtaining the interdependence relation between word vectors: obtaining the interdependence relation between each appeal text word by adopting a self-attention mechanism;
6) power service customer appeal text capsule aggregation: adopting an EM-Routing dynamic Routing algorithm to aggregate the word capsules into appeal text capsules to obtain semantic information relation among words;
7) calculating a loss value: and calculating loss values of the positive class and the negative class of the appeal text by adopting an interval loss function.
2. The capsule network-based power service client appeal text classification method of claim 1, wherein: the appealing text preprocessing process in the step 1) comprises the following steps: firstly, dividing all client appeal contents into reports, suggestions, prawns, complaints, queries, repair reports, past services and reflection categories according to text forms, wherein one line of text represents one client appeal text, text category labels are added, all symbols and stop words except letters, numbers and Chinese characters are deleted, and a jieba word segmentation tool is used for segmenting words of each line of appeal text.
3. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: the process of the bert pre-training model in the step 2) is as follows: each line of power supply service client appeal text after preprocessing
Figure 160900DEST_PATH_IMAGE001
Inputting the data into a bert pre-training model;
firstly, dynamic word vectors based on semantics are obtained
Figure 609199DEST_PATH_IMAGE002
Is shown asiDimension corresponding to each word is D1The vector of (a) is determined,
Figure 654516DEST_PATH_IMAGE003
the first in the text of each line of power supply client appealiIndividual word, L1Representing the number of words in each line of the power service client appeal text.
4. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: the encoding method based on the word vector in the step 3) comprises the following steps: set of words in per-line power-supply client appeal text
Figure 580883DEST_PATH_IMAGE004
Wherein
Figure 411567DEST_PATH_IMAGE005
The first in the text of each line of power supply client appealiWords, mapping the words to a high-dimensional space to obtain a word vector
Figure 499609DEST_PATH_IMAGE006
，
Figure 297801DEST_PATH_IMAGE007
Wherein
Figure 762280DEST_PATH_IMAGE008
Is shown asiDimension corresponding to each word is D2Word vector of, L2And the number of the words in the appeal text of each line of the power supply client is represented.
5. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: acquiring the dependency relationship between the characters in the step 3), which is specifically as follows:
3.1 generating phrase features: in a sentence, because each word is relatively independent, firstly, an N-garm convolution operation is adopted to obtain the interdependence relation between the words in the power supply client appeal text, namely the local features of the client appeal text, so as to generate phrase features;
suppose that
Figure 696738DEST_PATH_IMAGE009
Representing word vectors
Figure 221260DEST_PATH_IMAGE010
The process of the volume of the words is as follows:
Figure 991901DEST_PATH_IMAGE011
wherein
Figure 994492DEST_PATH_IMAGE012
，kWhich represents the size of the convolution kernel and,b 1a bias term is represented as a function of,
Figure 783457DEST_PATH_IMAGE013
the generated local phrase features are represented as,D 3indicates the number of phrases to be generated,frepresenting a ReLU nonlinear activation function, and performing convolution operation at different positions of a sentence to generateIs characterized by the phrase
Figure 744460DEST_PATH_IMAGE014
；
3.2 obtaining semantic features of phrase features: obtaining important semantic features through maximum pooling operation, wherein the pooling process comprises the following steps:
Figure 251664DEST_PATH_IMAGE015
wherein
Figure 792367DEST_PATH_IMAGE016
Is shown asiObtaining a characteristic value by performing maximum pooling on the features extracted by the convolution kernels;
3.3 eigenvalue stitching: splicing the obtained characteristic values to obtain an appeal text characteristic vector generated based on the word vector
Figure 717729DEST_PATH_IMAGE017
Wherein D is3The number of the characteristic values is represented,
Figure 318474DEST_PATH_IMAGE018
a text feature vector representing a word-based power client appeal.
6. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: the appeal text feature fusion method based on the word and word vector in the step 4) comprises the following steps: fusing the power supply client appeal text characteristic vectors based on characters and words to obtain a fusion vector
Figure 312975DEST_PATH_IMAGE019
Is shown asiA logical vector representation corresponding to a word, then
Figure 657369DEST_PATH_IMAGE020
。
7. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: step 5) obtaining the interdependence relation among the word vectors, which is as follows:
5.1 obtaining semantic vectors between words
Figure 420926DEST_PATH_IMAGE021
: first, calculate theiA word and ajSemantic information vector between words
Figure 723731DEST_PATH_IMAGE022
Wherein
Figure 939949DEST_PATH_IMAGE023
The information is represented by a transformation matrix of information,
Figure 841695DEST_PATH_IMAGE024
representing a fused vector
Figure 459758DEST_PATH_IMAGE025
The vector of Q-values of (a),
Figure 933465DEST_PATH_IMAGE026
representing a fused vector K value vector;
5.2 normalization calculation: computing collaborative weights for semantic information vectors by normalization operations
Figure 636979DEST_PATH_IMAGE027
Fused vector
Figure 323175DEST_PATH_IMAGE028
Is shown asjWord vectorhTo obtain a vector of values of
Figure 61324DEST_PATH_IMAGE029
。
8. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: step 6), the power supply service client appeals for text capsule aggregation, and the method specifically comprises the following steps:
6.1 calculating the probability Density
Figure 440353DEST_PATH_IMAGE030
: calculating high-level capsule asjIn time, the bottom layer capsuleiIn the first placehProbability density in one dimension
Figure 647474DEST_PATH_IMAGE031
To represent
Figure 137362DEST_PATH_IMAGE032
In thathThe value in the dimension(s) is,
Figure 464438DEST_PATH_IMAGE033
indicating high-level capsulesjIn thathMean value in dimension
Figure 279947DEST_PATH_IMAGE034
Indicating high-level capsulesjIn thathVariance in dimensionσ；
6.2 logarithm of probability Density
Figure 958053DEST_PATH_IMAGE035
；
6.3 encapsulating each of the bottom layersiIn thathVector value in dimension to high level capsulejVector value summation
Figure 251631DEST_PATH_IMAGE036
：
Figure 449525DEST_PATH_IMAGE038
Wherein
Figure 904777DEST_PATH_IMAGE039
Indicating allocation to higher level capsulesjThe total amount of data of (c);
6.4 calculate high level CapsulejActivation value of
Figure 335759DEST_PATH_IMAGE040
Also indicating a high level capsulejThe probability of the presence of the object,
Figure 167449DEST_PATH_IMAGE041
are all hyper-parameters.
9. The method for appealing to the client of the power supply service based on the capsule network as claimed in claim 1, wherein: step 7) calculating a loss value, and calculating the loss value with the distance of m between the positive class and the negative class of each line of power supply client appeal text by adopting an interval loss function
Figure 734696DEST_PATH_IMAGE042
The positive class is represented by a positive number,
Figure 360849DEST_PATH_IMAGE043
representing negative classes, and then adding the loss values to obtain a total loss value
Figure 29859DEST_PATH_IMAGE044
。
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CN113158679A (en) * 2021-05-20 2021-07-23 广东工业大学 Marine industry entity identification method and device based on multi-feature superposition capsule network
CN113590813A (en) * 2021-01-20 2021-11-02 腾讯科技（深圳）有限公司 Text classification method, recommendation device and electronic equipment
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