CN116596496A - Person post matching method, system, medium and equipment based on labeling - Google Patents
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Abstract
The invention belongs to the field of artificial intelligence, and provides a person post matching method, a system, a medium and equipment based on tagging, which are characterized in that through an artificial intelligence technology and a built-in rule engine, a post label and a person label are automatically extracted by analyzing and extracting a post qualification specification and unstructured materials based on a trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and the post label and the person label are returned to a specific label after being matched with the rule dictionary library; and finally, matching the personnel information label and the post information label according to the label mapping relation, wherein the matching degree reflects the personnel post matching condition.
Description
Technical Field
The invention belongs to the field of artificial intelligence, and particularly relates to a person post matching method, system, medium and equipment based on labeling.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The traditional sentry matching method is strong in subjectivity, and the matching result lacks standard for the standard; the manual workload is large when checking in a large-scale team; the advantage information and the deficiency information of the staff cannot be automatically analyzed, and the staff can be judged manually or summarized manually only by relying on subjective scores.
At present, although machine learning is adopted for post matching, the defects are that: the technical means of extracting and matching the label information of posts and resume by utilizing a neural network algorithm and establishing vector correlation can only meet the matching of personal resume with relatively fixed structure in recruitment scenes, but cannot meet the problems of label extraction and person post matching of a large number of unstructured materials of staff of enterprises.
Disclosure of Invention
In order to solve at least one technical problem in the background technology, the invention provides a person post matching method, a system, a medium and equipment based on labeling, which are used for carrying out labeling extraction through an artificial intelligence technology, and finally realizing the matching condition of a person information label and a post information label through a label mapping relation, wherein the matching degree reflects the person post matching condition.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a person post matching method based on labeling, which comprises the following steps:
acquiring a qualification specification of a required post and unstructured materials of staff;
analyzing and extracting the job title specification and unstructured materials based on the trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and after matching with the rule dictionary library, the specific label is returned to obtain a required post job information label and employee information label;
the logic for resolving and extracting labels from the qualification specification and unstructured materials is as follows: reading a file stream through codes, converting the file stream into an xml text, creating a temporary word segmentation table based on the xml text according to a word segmentation algorithm, calling a service interface of a deep learning model by taking the temporary word segmentation table as an input parameter, and returning a corresponding post or a corresponding employee information label by the interface;
and matching based on the required post optional information label and the employee information label to obtain a person post matching result.
Further, when the qualification specification and the unstructured material are analyzed and the label is extracted, the final corresponding post or corresponding employee information label is obtained through merging and de-duplication with the label extracted by the training model.
Further, the rule dictionary library comprises subject-adjective, verb-object combination key value pairs, white lists and black lists; wherein, the label is directly marked when the content in the white list appears in the white list realization statement; and when the corresponding content appears in the blacklist, the corresponding label is not marked.
Further, the deep learning model adopts a named entity recognition NER algorithm and a FastText algorithm of BERT-BiLTSM-CRF.
Further, when the optional information label based on the required post is matched with the existing information label of the staff, the existing information label on the post and the corresponding weight coefficient are matched with the existing information label on the staff based on the mapping relation table of the post information label library and the staff information label library.
Further, after the person post matching result is obtained, according to the matching degree condition and the definition of the label index, the development suggestion corresponding to the label which is not matched is pushed to staff.
Further, when the deep learning model is trained, thresholds of F1 value, accuracy and recall rate of the output model are set, and extraction results are filtered according to the thresholds.
A second aspect of the present invention provides a tagged based person post matching system, comprising:
a data acquisition module configured to: acquiring a qualification specification of a required post and unstructured materials of staff;
a tag extraction module configured to: analyzing and extracting the job title specification and unstructured materials based on the trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and after matching with the rule dictionary library, the specific label is returned to obtain a required post job information label and employee information label;
the logic for resolving and extracting labels from the qualification specification and unstructured materials is as follows: reading the file stream through codes, converting the file stream into an xml text, creating a temporary word segmentation table based on the xml text according to a word segmentation algorithm, calling a service interface of a deep learning model by taking the temporary word segmentation table as an input parameter, and returning an information label of a corresponding post or a corresponding employee by the interface;
a person post matching module configured to: and matching the optional information label based on the required post with the existing information label of the staff to obtain a person post matching result.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a person post matching method based on labelling as described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in a tag-based person post matching method as described above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, through an artificial intelligence technology and a built-in rule engine, the qualification specification and unstructured materials are analyzed and the labels are extracted, each built-in label corresponds to a corresponding rule dictionary library, and the labels are returned to specific labels after being matched with the rule dictionary library, so that automatic extraction of post labels and personnel labels is realized.
2. The invention is based on the mapping relation table of the post information label library and the personnel information label library, and the existing information labels on the posts and the corresponding weight coefficients are matched with the existing information labels on the staff, so that the matching accuracy is improved, the workload of manual combing and summarizing is reduced, and the working efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is an overall flow chart of a person post matching method based on tagging in accordance with an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. 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 present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
As shown in fig. 1, the embodiment provides a person post automatic matching method based on labelling, which comprises the following steps:
step 1: the post information label library and the mapping relation table of the personnel information label library.
In this embodiment, the post information tag and the personnel information tag each include a quality tag, a performance tag and a character tag which are classified into a tag library of 200 remaining items, and the post information tag and the personnel information tag respectively belong to different tag libraries;
each post label item can be provided with a comparison relation with a personnel sub label item, and the comparison relation is used for automatically calculating the matching degree when the personnel posts are matched.
Step 2: acquiring a qualification specification of a required post and unstructured materials of staff;
unstructured material of the staff includes relevant research reports, real-world performance materials and the like.
Step 3: model training is carried out through machine learning;
when the deep learning model is trained, a NER algorithm and a FastText algorithm tool are identified based on named entities of BERT-BiLTSM-CRF of the deep learning framework, and a NER model training task is executed. Parameters such as training time length, batch size, epoch and the like are adjusted through a visual interface, and thresholds of indexes such as F1 value, accuracy, recall rate and the like of the output model are specified. And may publish the model as a service online or extract the model. And performing deep learning training on the selected marked sample by adopting a machine learning algorithm to finally form a model for application of the label extraction scene. After being packaged as a service interface and called, the trained model can be used for converting unstructured corpus into a scene of corresponding information labels.
Step 4: built-in rule engine
Labels may be automatically extracted from job title specifications for the desired post and unstructured material for the employee based on a specified rule field library, which may be in addition to the extracted labels of the machine learning training model. The rule engine comprises a rule dictionary library and a rule calling service. Each built-in label corresponds to a corresponding rule dictionary, and the rule dictionary comprises a subject-adjective, a verb-object combination key value pair, a white list and a black list; the white list realization statement is marked with the label directly when some contents in the white list appear; the content in the blacklist can ensure that the corresponding label cannot be marked when the corresponding content appears.
Step 5: label extraction
Through the trained model, the system can automatically analyze the qualification specifications of corresponding posts, extract corresponding labels and support setting different weight coefficients (default equal scores) for each label according to the priority. Meanwhile, unstructured materials such as relevant investigation reports, real representation materials and the like of personnel can be automatically extracted through corresponding training models.
The analysis and label extraction logic for attachments such as post qualification specification materials and trunk investigation materials are as follows: reading the file stream through codes, converting the file stream into xml text, creating a temporary word segmentation table according to a word segmentation algorithm, calling a service interface of a training model by taking the temporary word segmentation table as an input parameter, and returning an information label of a corresponding post or a corresponding person by the interface. According to the F1 value, the accuracy, the recall rate and the like which are output according to the model, the extraction result with poor quality can be automatically filtered. The accuracy of the labels returned by the training model generally depends on the number of samples used for learning training and the quality of the labels.
The word segmentation result of the unstructured material is transferred through calling the interface service of the rule engine, is matched with the rule dictionary library and then returns to a specific label, and the label extracted by the training model is combined and de-duplicated, so that the extraction precision of the label is greatly improved.
Step 6: automatic matching of person posts
Based on the mapping relation between the post label and the personnel label, the existing label on the post and the corresponding weight coefficient are matched with the existing label on the staff, the matching result is the matching degree of the personnel post information, and the bigger the result value is, the higher the matching degree of the staff and the post is.
And pushing the development suggestions corresponding to the tags which are not matched to the staff according to the matching degree condition and the definition of the tag indexes.
Example two
The embodiment provides a person post matching system based on labeling, which comprises:
a data acquisition module configured to: acquiring a qualification specification of a required post and unstructured materials of staff;
a tag extraction module configured to: analyzing and extracting the job title specification and unstructured materials based on the trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and after matching with the rule dictionary library, the specific label is returned to obtain the job information label of the required post and the existing information label of staff;
the logic for resolving and extracting labels from the qualification specification and unstructured materials is as follows: reading the file stream through codes, converting the file stream into an xml text, creating a temporary word segmentation table based on the xml text according to a word segmentation algorithm, calling a service interface of a deep learning model by taking the temporary word segmentation table as an input parameter, and returning an information label of a corresponding post or a corresponding employee by the interface;
a person post matching module configured to: and matching the optional information label based on the required post with the existing information label of the staff to obtain a person post matching result.
Example III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a person post matching method based on labelling as described above.
Example IV
The embodiment provides a computer device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the steps in the label-based person post matching method are realized when the processor executes the program.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The person post matching method based on the labeling is characterized by comprising the following steps of:
acquiring a qualification specification of a required post and unstructured materials of staff;
analyzing and extracting the job title specification and unstructured materials based on the trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and after matching with the rule dictionary library, the specific label is returned to obtain the job information label of the required post and the existing information label of staff;
the logic for resolving and extracting labels from the qualification specification and unstructured materials is as follows: reading the file stream through codes, converting the file stream into an xml text, creating a temporary word segmentation table based on the xml text according to a word segmentation algorithm, calling a service interface of a deep learning model by taking the temporary word segmentation table as an input parameter, and returning an information label of a corresponding post or a corresponding employee by the interface;
and matching the optional information label based on the required post with the existing information label of the staff to obtain a person post matching result.
2. The method for matching person posts based on labeling of claim 1, wherein when the job specification and unstructured material are analyzed and labels are extracted, final information labels corresponding to posts or corresponding staff are obtained by combining and de-duplicating the labels extracted by the training model.
3. The tagged human post matching method of claim 1, wherein the rule dictionary library comprises subject-adjective, verb-object combined key value pairs, whitelist, and blacklist; wherein, the label is directly marked when the content in the white list appears in the white list realization statement; and when the corresponding content appears in the blacklist, the corresponding label is not marked.
4. The tagged person post matching method of claim 1, wherein the deep learning model employs a named entity recognition NER algorithm and a FastText algorithm of BERT-BiLTSM-CRF.
5. The method for matching person posts based on labeling as set forth in claim 1, wherein when the post-based optional information labels based on the required posts are matched with the existing information labels of the staff, the post-based information labels and the existing information labels of the staff are matched through the existing information labels and the corresponding weight coefficients on the posts based on a mapping relation table of a post information label library and a personnel information label library.
6. The person post matching method based on the labeling of claim 1, wherein after the person post matching result is obtained, development suggestions corresponding to the labels which are not matched are pushed to staff according to the matching degree condition and the definition of label indexes.
7. The person post matching method based on labeling of claim 1, wherein the threshold values of the F1 value, the accuracy and the recall rate of the output model are set when the deep learning model is trained, and the extraction result is filtered according to the threshold values.
8. A person post matching system based on tagging, comprising:
a data acquisition module configured to: acquiring a qualification specification of a required post and unstructured materials of staff;
a tag extraction module configured to: analyzing and extracting the job title specification and unstructured materials based on the trained deep learning model and the built-in rule engine, wherein each built-in label corresponds to a corresponding rule dictionary library, and after matching with the rule dictionary library, the specific label is returned to obtain the job information label of the required post and the existing information label of staff;
the logic for resolving and extracting labels from the qualification specification and unstructured materials is as follows: reading the file stream through codes, converting the file stream into an xml text, creating a temporary word segmentation table based on the xml text according to a word segmentation algorithm, calling a service interface of a deep learning model by taking the temporary word segmentation table as an input parameter, and returning an information label of a corresponding post or a corresponding employee by the interface;
a person post matching module configured to: and matching the optional information label based on the required post with the existing information label of the staff to obtain a person post matching result.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the steps of a person post matching method based on labelling as claimed in any of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of a tagged person post matching method as claimed in any one of claims 1 to 7 when the program is executed.
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