CN114492448A - Method and system for determining intelligent semantic analysis model - Google Patents
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Abstract
The invention discloses a method and a system for determining an intelligent semantic analysis model, which comprises the following steps: acquiring first marking data used for training and algorithm logics corresponding to different types of models; determining the type of a required intelligent semantic analysis model according to the application of the first labeling data, and determining algorithm logic corresponding to the first labeling data according to the type of the intelligent semantic analysis model; training based on the first labeling data and algorithm logic corresponding to the first labeling data to obtain the intelligent semantic analysis model. The method can be widely applied to the development process of various semantic analysis models, can improve the development efficiency, reduce the time overhead, enable developers to concentrate more energy on specific difference links, and develop the corresponding stable models more efficiently aiming at different application scenes, thereby obtaining better semantic analysis effect.
Description
Technical Field
The present invention relates to the field of artificial intelligence technology, and more particularly, to a method and system for determining an intelligent semantic analysis model.
Background
Semantic Analysis (Semantic Analysis) is a branch of Artificial Intelligence (Artificial Intelligence), is a plurality of core tasks of natural language processing technology, relates to multiple subjects such as linguistics, computational linguistics, machine learning and cognitive language, and helps promote the rapid development of other natural language processing tasks. Semantic analysis technology in artificial intelligence, especially Deep Learning (Deep Learning) technology, has developed rapidly in recent years, and has made breakthrough progress in a plurality of fields such as go playing, automatic driving, image recognition, voice recognition, and the like. Brings great economic benefits to many industries and brings many changes and conveniences to our lives. With the successful application of artificial intelligence semantic analysis in various fields and the deep development of technology, semantic analysis algorithms in numerous subdivided fields are derived, and links such as model training, test analysis, effect preview and the like have certain differences in the model development process of different algorithms, so that developers more or less increase the cost such as time, energy and the like in the development of different models.
Therefore, a method for developing a stable model corresponding to different application scenarios more efficiently is needed.
Disclosure of Invention
The invention provides a method and a system for determining an intelligent semantic analysis model, which aim to solve the problem of how to rapidly determine the intelligent semantic analysis model.
In order to solve the above problem, according to an aspect of the present invention, there is provided a method of determining an intelligent semantic analysis model, the method including:
acquiring first marking data used for training and algorithm logics corresponding to different types of models;
determining the type of a required intelligent semantic analysis model according to the application of the first labeling data, and determining algorithm logic corresponding to the first labeling data according to the type of the intelligent semantic analysis model;
training based on the first labeling data and algorithm logic corresponding to the first labeling data to obtain the intelligent semantic analysis model.
Preferably, wherein the method further comprises:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
Preferably, wherein the method further comprises:
displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode to realize online input parameters and interface calling, acquiring a model analysis result returned by an interface in the browser interface mode, and storing the model analysis result.
Preferably, wherein the method further comprises:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
Preferably, wherein the method further comprises:
acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a test result, and determining the quality of the intelligent semantic analysis model based on the test result; wherein the test results include: and (4) accuracy.
According to another aspect of the present invention, there is provided a system for determining an intelligent semantic analysis model, the system comprising:
the data storage module is used for acquiring first marking data used for training and algorithm logics corresponding to different types of models;
the algorithm logic determination module is used for determining the type of the required intelligent semantic analysis model according to the application of the first labeled data and determining the algorithm logic corresponding to the first labeled data according to the type of the intelligent semantic analysis model;
and the training module is used for training based on the first labeling data and the algorithm logic corresponding to the first labeling data so as to obtain the intelligent semantic analysis model.
Preferably, wherein the system further comprises: a data processing module to:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
Preferably, wherein the system further comprises:
and the display module is used for displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode so as to realize online input parameters and interface calling, acquiring a model analysis result returned by the interface in the browser interface mode, and storing the model analysis result.
Preferably, wherein the display module further comprises:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
Preferably, wherein the system further comprises:
the testing module is used for acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a testing result and determining the quality of the intelligent semantic analysis model based on the testing result; wherein the test results include: and (4) accuracy.
The invention provides a method and a system for determining an intelligent semantic analysis model, which can complete the integration of common modules of similar steps of different models in the development process, realize the pluggable effect of high cohesion and low coupling by introducing and configuring difference steps in a minimized way, can be widely applied to the development process of various semantic analysis models, can improve the development efficiency and reduce the time overhead, and ensure that developers can concentrate more energy on specific difference links, more efficiently develop a stable model corresponding to the different application scenes and obtain better semantic analysis effect.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow diagram of a method 100 of determining an intelligent semantic analysis model according to an embodiment of the invention;
FIG. 2 is an architecture diagram of a development platform for determining an intelligent semantic analysis model according to an embodiment of the present invention;
FIG. 3 is a block diagram of a system 300 for determining an intelligent semantic analysis model according to an embodiment of the invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same unit/element is denoted by the same reference numeral.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow diagram of a method 100 of determining an intelligent semantic analysis model according to an embodiment of the invention. As shown in fig. 1, the method for determining an intelligent semantic analysis model according to the embodiment of the present invention can complete the integration of common modules of different models in similar steps during the development process, and implement a pluggable effect with high cohesion and low coupling by introducing and configuring the difference steps in a minimized manner, and can be widely applied to the development process of various semantic analysis models, thereby improving the development efficiency, reducing the time overhead, enabling developers to concentrate more efforts on specific difference links, developing a stable model corresponding to different application scenarios more efficiently, and obtaining a better semantic analysis effect. The method 100 for determining the intelligent semantic analysis model provided by the embodiment of the invention starts from step 101, and obtains first annotation data for training and algorithm logics corresponding to different types of models in step 101.
Preferably, wherein the method further comprises:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
In the invention, the algorithm logic corresponding to various models, the first marking data used for training and the second marking data used for testing are obtained and stored. The unlabeled data set is preprocessed through data cleaning, manual labeling and the like, and the labeled data set is divided into labeled data for testing and labeled data for training according to 28 principles (namely 20% of test data and 80% of training data). The data storage module can acquire the required marking data and algorithm from the data storage module.
In step 102, the type of the required intelligent semantic analysis model is determined according to the purpose of the first labeled data, and the algorithm logic corresponding to the first labeled data is determined according to the type of the intelligent semantic analysis model.
In step 103, training is performed based on the first labeled data and the algorithm logic corresponding to the first labeled data to obtain the intelligent semantic analysis model.
In the invention, the type of the required intelligent semantic analysis model is determined according to the purpose of the labeled data, a corresponding algorithm is determined according to the type of the intelligent semantic analysis model, then the preset related algorithm logic is called, training and model generation are carried out according to the first labeled data, and the training and model generation are simultaneously stored in a model module.
Preferably, wherein the method further comprises:
displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode to realize online input parameters and interface calling, acquiring a model analysis result returned by an interface in the browser interface mode, and storing the model analysis result.
Preferably, wherein the method further comprises:
acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a test result, and determining the quality of the intelligent semantic analysis model based on the test result; wherein the test results include: and (4) accuracy.
In the invention, the model can be respectively issued to the display module and the test module to carry out effect demonstration, joint debugging test and unit test. The display module displays the address of the model calling interface in a browser interface mode, supports online parameter filling and interface calling, can see the prediction result returned by the interface in real time through the browser interface, and automatically stores the result in the data module and provides a downloading function, so that model effect demonstration, joint debugging test in front-end and back-end separation modes and prediction result downloading are realized. In addition, the test module can use the test label data transmitted by the data module to perform unit test and generate a test result, and a developer can judge the quality of the model by analyzing the result.
Preferably, wherein the method further comprises:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
In the invention, if the contents of the prediction results of some models are more, a developer defines the prediction results as file form output, but directly returns the contents in json format through the http response result, the file can be automatically saved in the data module, and a file download address is output in the http response result so as to be downloaded and analyzed at any time or store the result.
FIG. 2 is an architecture diagram of a development platform for determining an intelligent semantic analysis model according to an embodiment of the invention. As shown in fig. 2, includes:
and the data module is used for uploading and downloading labeled data such as training data, testing data and the like, providing interfaces for supporting related data for the training module and the testing module, and storing and downloading model prediction results.
And the training module is used for calling related training data, introducing a built-in algorithm source code, training and generating a corresponding algorithm model.
The algorithm module is internally provided with various commonly used algorithms such as XGboost, LSTM and the like, and simultaneously supports manual maintenance and increase and decrease of the algorithms. And providing a calling interface for storing all algorithms to support the calling of the training module and finish the training of each model.
And the model module is used for storing the model generated by the training module, supporting the model to be respectively issued to the display module and the test module, and realizing the functions of model effect display, unit and joint debugging test and the like.
And the test module is used for calling the test marking data of the data module, performing unit test by using the model issued by the model module, generating a test result with indexes such as accuracy and the like, and using the test result for subsequent analysis.
And the display module supports joint debugging test work of development modes such as online display of model effects in a webpage mode, front-end and back-end separation and the like. The developer can visually see the model and the interface calling address thereof issued by the model module on the browser, can directly fill parameters online to carry out interface calling, and can timely see the returned result of model prediction. If the contents of the prediction results of some models are more, a developer defines that the prediction results are output in a file form, but directly returns the contents in a json format through the http response result, the file can be automatically stored in the data module, and a file downloading address is output in the http response result so as to be downloaded and analyzed at any time or store the result.
The invention integrates the similar steps of different models in the development process into a universal module, such as a training model, a model module, a test module, a display module and the like. And the pluggable effect of high cohesion and low coupling is realized by introducing and configuring differentiated steps in a minimized way, such as an algorithm module, a data module and the like. A unique development mode and specification are creatively provided, and a rapid development platform of various semantic analysis models is realized by combining a Python-based Django Rest Framework open source Framework.
The method for determining the intelligent semantic analysis module can extract similar steps to arrange difference links, forms a modular universal semantic analysis model development platform architecture by combining a mature Python open source framework through a unique development mode and specifications, reduces time and energy costs in the starting process of different semantic analysis models, enables developers to concentrate on business rather than technical detail difference, and improves development efficiency.
FIG. 3 is a block diagram of a system 300 for determining an intelligent semantic analysis model according to an embodiment of the invention. As shown in fig. 3, a system 300 for determining an intelligent semantic analysis model according to an embodiment of the present invention includes: a data storage module 301, an algorithmic logic determination module 302, and a training module 303.
Preferably, the data storage module 301 is configured to obtain the first labeling data for training and the algorithm logic corresponding to the different types of models.
Preferably, wherein the system further comprises: a data processing module to:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
Preferably, the algorithm logic determining module 302 is configured to determine the type of the required intelligent semantic analysis model according to the usage of the first labeled data, and determine the algorithm logic corresponding to the first labeled data according to the type of the intelligent semantic analysis model.
Preferably, the training module 303 is configured to train based on the first labeled data and an algorithm logic corresponding to the first labeled data to obtain the intelligent semantic analysis model.
Preferably, wherein the system further comprises:
and the display module is used for displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode so as to realize online input parameters and interface calling, acquiring a model analysis result returned by the interface in the browser interface mode, and storing the model analysis result.
Preferably, wherein the display module further comprises:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
Preferably, wherein the system further comprises:
the testing module is used for acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a testing result and determining the quality of the intelligent semantic analysis model based on the testing result; wherein the test result comprises: and (4) accuracy.
The system 300 for determining an intelligent semantic analysis model according to an embodiment of the present invention corresponds to the method 100 for determining an intelligent semantic analysis model according to another embodiment of the present invention, and is not described herein again.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method of determining an intelligent semantic analysis model, the method comprising:
acquiring first marking data used for training and algorithm logics corresponding to different types of models;
determining the type of a required intelligent semantic analysis model according to the application of the first labeling data, and determining algorithm logic corresponding to the first labeling data according to the type of the intelligent semantic analysis model;
training based on the first labeling data and algorithm logic corresponding to the first labeling data to obtain the intelligent semantic analysis model.
2. The method of claim 1, further comprising:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
3. The method of claim 1, further comprising:
displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode to realize online input parameters and interface calling, acquiring a model analysis result returned by an interface in the browser interface mode, and storing the model analysis result.
4. The method of claim 3, further comprising:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
5. The method of claim 1, further comprising:
acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a test result, and determining the quality of the intelligent semantic analysis model based on the test result; wherein the test result comprises: and (4) accuracy.
6. A system for determining an intelligent semantic analysis model, the system comprising:
the data storage module is used for acquiring first marking data used for training and algorithm logics corresponding to different types of models;
the algorithm logic determination module is used for determining the type of the required intelligent semantic analysis model according to the application of the first labeled data and determining the algorithm logic corresponding to the first labeled data according to the type of the intelligent semantic analysis model;
and the training module is used for training based on the first labeling data and the algorithm logic corresponding to the first labeling data so as to obtain the intelligent semantic analysis model.
7. The system of claim 6, further comprising: a data processing module to:
carrying out data cleaning on the unmarked data set, and marking the unmarked data set subjected to data cleaning according to the purpose of the marked data to obtain a marked data set;
and randomly selecting the labeled data set with a preset percentage threshold value as the first labeled data, and using the rest labeled data sets as second labeled data for testing.
8. The system of claim 6, further comprising:
and the display module is used for displaying the calling interface address of the intelligent semantic analysis model in a browser interface mode so as to realize online input parameters and interface calling, acquiring a model analysis result returned by the interface in the browser interface mode, and storing the model analysis result.
9. The system of claim 8, wherein the display module further comprises:
and when the space occupied by the model analysis result is more than or equal to the preset occupied space capacity, returning a file in a json format through the http response result, and outputting a file downloading address in the http response result for downloading analysis or storing the result.
10. The system of claim 6, further comprising:
the testing module is used for acquiring second labeling data for testing, testing the intelligent semantic analysis model by using the second labeling data, acquiring a testing result and determining the quality of the intelligent semantic analysis model based on the testing result; wherein the test results include: and (4) accuracy.
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| CN112035622A (en) * | 2020-09-04 | 2020-12-04 | 上海明略人工智能(集团)有限公司 | Integrated platform and method for natural language processing |
| CN112445845A (en) * | 2020-12-02 | 2021-03-05 | 平安科技（深圳）有限公司 | Model deployment method, device, equipment and storage medium based on big data mining |
| CN113760464A (en) * | 2021-09-14 | 2021-12-07 | 中化现代农业有限公司 | Artificial intelligence model development platform based on deep learning |
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| CN110705271A (en) * | 2019-09-27 | 2020-01-17 | 中国建设银行股份有限公司 | System and method for providing natural language processing service |
| CN111126626A (en) * | 2019-12-24 | 2020-05-08 | 上海器魂智能科技有限公司 | Training method, device, server, platform and storage medium |
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