CN113836304A - Intelligent labeling method and system based on natural language processing - Google Patents
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Abstract
The invention discloses an intelligent labeling method and system based on natural language processing, which comprises the following steps: constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels; packaging the constructed label model to generate an interface; configuring an interface; acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed. The implementation of the invention realizes the automation of the whole process of model construction, test, online, execution and text labeling for the text labeling task, greatly improves the working efficiency, saves the labor cost of companies, and simultaneously obtains higher accuracy through verification.
Description
Technical Field
The invention relates to the technical field of intelligent tags, in particular to an intelligent tagging method and system based on natural language processing.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The text information data in the big data era is greatly appeared, more and more text data enter a storage database for unified storage, and how to uniformly manage and label the massive text data in the database is the problem which needs to be mainly solved at present. However, most of the existing text labeling methods focus on processing only single field text content, do not realize the construction of a complete text information labeling process for all fields in a database, and simultaneously lack closed-loop management of text labeling tasks. Therefore, an automatic text labeling method is required to be constructed to perform labeling processing on a large amount of texts in combination with the actual application scene of the text labeling task, and meanwhile, the realization of the closed loop of the text labeling task is the core problem of the task.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides an intelligent labeling method and system based on natural language processing; firstly, a database text labeling task flow is established, then a text label processing model based on natural language processing is established, corresponding models are selected for matching text data to be processed in the database to complete label task creation, and finally text intelligent labeling is automatically carried out according to a task execution rule. Therefore, the intelligent labeling of the text information data in the database is realized by adopting natural language processing.
In a first aspect, the invention provides an intelligent labeling method based on natural language processing;
an intelligent labeling method based on natural language processing comprises the following steps:
constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
packaging the constructed label model to generate an interface;
configuring an interface;
acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
In a second aspect, the invention provides an intelligent labeling system based on natural language processing;
an intelligent labeling system based on natural language processing, comprising:
a build module configured to: constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
a packaging module configured to: packaging the constructed label model to generate an interface;
a configuration module configured to: configuring an interface;
a labeling module configured to: acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
In a third aspect, the present invention further provides an electronic device, including:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention also provides a storage medium storing non-transitory computer readable instructions, wherein the non-transitory computer readable instructions, when executed by a computer, perform the instructions of the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
in actual work, the manual data labeling work is time-consuming and labor-consuming, but after the label model is introduced, only a part of manual operation can be reduced. Especially, after the model is updated or the labeling data is updated, the existing label model and the existing label labeling method cannot be reused, which causes waste of the labor cost and the development cost of the company. The implementation of the invention enables the text labeling task to print one label for a plurality of fields at the same time, realizes the automation of the whole process of model construction, test, online, execution and text labeling, greatly improves the working efficiency, saves the labor cost of companies, and obtains higher accuracy through verification.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the method of the first embodiment.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all 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.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
All data are obtained according to the embodiment and are legally applied on the data on the basis of compliance with laws and regulations and user consent.
Example one
The embodiment provides an intelligent labeling method based on natural language processing;
as shown in fig. 1, an intelligent labeling method based on natural language processing includes:
s101: constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
s102: packaging the constructed label model to generate an interface;
s103: configuring an interface;
s104: acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
For example, there are a plurality of fields in a certain enterprise employee basic information data a: a plurality of fields such as departments, native places, colleges and universities, academic calendars and titles; current technical research is mainly focused on obtaining a tag based on only one field, for example: obtaining a "student" label according to the "school calendar"; the method combines a specific service scene to obtain a label according to a plurality of fields, for example: dimension labels of the enterprises in the middle-level job title researchers or the primary job title subjects are obtained according to the departments, the academic calendars and the titles, so that the data analysis dimension is increased. Further, S101: constructing a training set and a test set; the method specifically comprises the following steps:
acquiring text data of a known label;
carrying out data cleaning on the text data of the known label, and deleting a blank space, a line-changing character, an exclamation mark and a special character in the text data in the data cleaning process; wherein, the special character refers to "+" or "#";
segmenting the data after data cleaning into a plurality of text entries;
and dividing the obtained text entries into a training set and a test set according to a proportion.
Illustratively, the data cleansing is implemented by using a place function.
Illustratively, the data after the data washing is cut into a plurality of text entries, and a tailor function is adopted to realize the cut.
Further, S101: constructing a label model based on the training set and the test set; the method specifically comprises the following steps:
performing feature extraction on text data in a training set by adopting a language representation model (BERT) to obtain text features;
training the convolutional neural network based on the text features and the corresponding labels in the training set to obtain a trained convolutional neural network, and taking the trained convolutional neural network as a label model;
testing the trained convolutional neural network based on the text features and the corresponding labels in the test set to obtain the label printing accuracy of the model;
if the accuracy rate exceeds a set threshold, finishing training, and taking the obtained trained convolutional neural network as a label model; and if the accuracy is lower than the set threshold value, retraining.
Further, S102: packaging the constructed label model to generate an interface; the method specifically comprises the following steps:
s1021: defining a label model request method, realizing the definition of request parameters, outputting corresponding scores of a returned parameter text label and a returned parameter text label, and finishing the encapsulation of the label model request method;
s1022: and starting the constructed label model and the packaged label model request method through a python command to generate a label model calling interface.
Further, S103: configuring an interface; the method specifically comprises the following steps:
s1031: calling an API (application programming interface) according to the tag model, and constructing a model visual configuration function by adopting an ElementUI method through the VUE framework;
s1032: and obtaining a tag model return parameter according to the address of the model calling interface generated in the step S1022 and the tag model request method defined in the step S1021, and storing the tag model return parameter according to the defined text tag label and the parameter of the score corresponding to the text tag label to complete the visual interface configuration of the tag model.
Further, S104: acquiring text data to be processed; calling configuration, and labeling the natural language to be processed; the method specifically comprises the following steps:
s1041: introducing a VUE framework, setting a label task name, a model calling interface, a request parameter and a return parameter by adopting an ElementUI method, and completing the function configuration of the label task;
s1042: configuring a label task name, a model calling interface, request parameters, return parameters and label field information according to the label task configuration function constructed in S1041, the label model interface constructed in S1022 and the text content to be labeled;
s1043: acquiring input data information to be labeled, and configuring an output table name and an output table field of labeled data;
s1044: a distributed workflow scheduling graph is constructed by adopting a Dolphin scheduler, so that the automatic starting and stopping of a labeling task are realized, and labeling is completed.
The invention designs an intelligent labeling system and method based on natural language processing. Firstly, a natural language processing technology is adopted to construct a label model based on Bert + rules, then a text labeling task is constructed, and the task construction is completed by selecting data to be labeled and the label model. And finally, automatically executing the intelligent label task to finish the label printing operation.
Example two
The embodiment provides an intelligent labeling system based on natural language processing;
an intelligent labeling system based on natural language processing, comprising:
a build module configured to: constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
a packaging module configured to: packaging the constructed label model to generate an interface;
a configuration module configured to: configuring an interface;
a labeling module configured to: acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
It should be noted here that the building module, the packaging module, the configuration module and the labeling module correspond to steps S101 to S104 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent labeling method based on natural language processing is characterized by comprising the following steps:
constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
packaging the constructed label model to generate an interface;
configuring an interface;
acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
2. The intelligent labeling method based on natural language processing as claimed in claim 1, wherein a training set and a test set are constructed; the method specifically comprises the following steps:
acquiring text data of a known label;
carrying out data cleaning on the text data of the known label, and deleting a blank space, a line-changing character, an exclamation mark and a special character in the text data in the data cleaning process;
segmenting the data after data cleaning into a plurality of text entries;
and dividing the obtained text entries into a training set and a test set according to a proportion.
3. The intelligent labeling method based on natural language processing as claimed in claim 1, wherein a label model is constructed based on a training set and a test set; the method specifically comprises the following steps:
extracting the features of the text data in the training set by adopting a language representation model to obtain text features;
training the convolutional neural network based on the text features and the corresponding labels in the training set to obtain a trained convolutional neural network, and taking the trained convolutional neural network as a label model;
testing the trained convolutional neural network based on the text features and the corresponding labels in the test set to obtain the label printing accuracy of the model;
if the accuracy rate exceeds a set threshold, finishing training, and taking the obtained trained convolutional neural network as a label model; and if the accuracy is lower than the set threshold value, retraining.
4. The intelligent labeling method based on natural language processing as claimed in claim 1, wherein the constructed label model is encapsulated to generate an interface; the method specifically comprises the following steps:
defining a label model request method, realizing the definition of request parameters, outputting corresponding scores of a returned parameter text label and a returned parameter text label, and finishing the encapsulation of the label model request method;
and starting the constructed label model and the packaged label model request method through a python command to generate a label model calling interface.
5. The intelligent labeling method based on natural language processing as claimed in claim 1, wherein the interface is configured; the method specifically comprises the following steps:
calling an API (application programming interface) according to the tag model, and constructing a model visual configuration function by the VUE framework in an ElementUI mode;
and acquiring a label model return parameter according to the generated address of the model calling interface and the defined label model request method, and storing the label model return parameter according to the defined text label and the parameter of the corresponding score of the text label to complete the visual interface configuration of the label model.
6. The intelligent labeling method based on natural language processing as claimed in claim 4, wherein the text data to be processed is obtained; calling configuration, and labeling the natural language to be processed; the method specifically comprises the following steps:
introducing a VUE framework, setting a label task name, a model calling interface, a request parameter and a return parameter by adopting an ElementUI mode, and completing the function configuration of the label task;
configuring a label task name, a model calling interface, a request parameter, a return parameter and label field information according to a constructed label task configuration function, a constructed label model calling interface and text content of a label to be marked;
acquiring input data information to be labeled, and configuring an output table name and an output table field of labeled data;
a distributed workflow scheduling graph is constructed by adopting a Dolphin scheduler, so that the automatic starting and stopping of a labeling task are realized, and labeling is completed.
7. The intelligent labeling method based on natural language processing as claimed in claim 2, wherein said data washing is implemented by using a replace function; and the data after the data cleaning is cut into a plurality of text entries, and is realized by adopting a tailor function.
8. An intelligent labeling system based on natural language processing is characterized by comprising:
a build module configured to: constructing a training set and a test set; constructing a label model based on the training set and the test set; wherein the training set and the test set are all a plurality of field texts of known labels;
a packaging module configured to: packaging the constructed label model to generate an interface;
a configuration module configured to: configuring an interface;
a labeling module configured to: acquiring text data to be processed; and calling configuration, and labeling the natural language to be processed.
9. An electronic device, comprising:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform a natural language processing based intelligent tagging method according to any one of claims 1 to 7.
10. A storage medium storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the instructions of the intelligent labeling method based on natural language processing of any one of claims 1 to 7.
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