Resume named entity identification method and system

A named entity recognition and named entity technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as reducing the amount of calculation, unable to obtain two-way information of sentences, increasing the data volume of training sets, etc., to enhance semantics. The effect of representing, improving the accuracy of label prediction, and improving the efficiency of information recognition

Active Publication Date: 2021-08-03
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Chinese invention patent (application number: CN109800437A, patent name: a named entity recognition method based on feature fusion), through feature fusion after extracting feature semantics, word features, and character features, classify entity information and improve named entity classification accuracy and reduce the amount of calculation, but due to the adoption of the LSTM network, the two-way information of the sentence cannot be obtained. Although the amount of calculation is saved, the data volume requirement for the training set is increased at the same time.

Method used

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  • Resume named entity identification method and system
  • Resume named entity identification method and system
  • Resume named entity identification method and system

Examples

Experimental program
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Effect test

Embodiment 1

[0049] This embodiment provides a resume named entity recognition method;

[0050] Such as figure 1 As shown, the resume named entity recognition method includes:

[0051] S101: Acquiring resumes to be processed;

[0052] S102: Preprocessing the resume to be processed;

[0053] S103: Match the preprocessed resume with the entities in the custom entity dictionary library one by one to obtain a first predicted named entity set that matches successfully; wherein, the first predicted named entity set includes: several named entities;

[0054] S104: Input the preprocessed resume into the trained resume named entity recognition model to obtain a second predicted named entity set; wherein, the second predicted named entity set includes: several named entities; resume named entity recognition model , including: BiLSTM model connected to each other and conditional random field model CRF;

[0055] S105: Take a union of the first predicted named entity set and the second predicted na...

Embodiment 2

[0154] This embodiment provides a resume named entity recognition system;

[0155] Resume Named Entity Recognition System, including:

[0156] an acquisition module configured to: acquire resumes to be processed;

[0157] A preprocessing module configured to: preprocess the resume to be processed;

[0158] The matching module is configured to: match the preprocessed resume with the entities in the custom entity dictionary library one by one, and obtain the first predicted named entity set that matches successfully; wherein, the first predicted named entity set includes: several named entities;

[0159] The prediction module is configured to: input the preprocessed resume into the trained resume named entity recognition model to obtain a second predicted named entity set; wherein, the second predicted named entity set includes: several named entities ;Resume named entity recognition model, including: BiLSTM model connected to each other and conditional random field model CRF...

Embodiment 3

[0166] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0167] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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Abstract

The invention discloses a resume named entity recognition method and system. The resume named entity recognition method comprises the steps of obtaining a to-be-processed resume; preprocessing the resume to be processed; matching the preprocessed resume with entities in a self-defined entity dictionary library one by one to obtain a successfully matched first predicted named entity set; inputting the preprocessed resume into a trained resume named entity recognition model to obtain a second predicted named entity set; taking a union set of the first predicted named entity set and the second predicted named entity set to obtain a merged predicted named entity set; taking the named entities in the merged predicted named entity set as a final named entity recognition result of the to-be-processed resume; and generating a knowledge graph based on the final named entity recognition result of the to-be-processed resume. Data are displayed and stored in a more novel mode, and help is provided for resume information labeling.

Description

technical field [0001] The invention relates to the technical field of machine learning and knowledge graphs, in particular to a method and system for recognizing named entities in resumes. Background technique [0002] The statements in this section merely mention the background technology related to the present invention and do not necessarily constitute the prior art. [0003] In recent years, with the rapid increase of graduates and the increasing number of application resumes, the efficiency issues have attracted more and more attention. Some medium and large companies receive hundreds or even thousands of resumes. It will take a lot of time and energy to find out what kind of abilities the applicants have in the resumes by manpower. If the award-winning experience and internship experience in the resume can be marked, Form a visual resume, which will be very convenient to check. [0004] Chinese invention patent (application number: CN109800437A, patent name: a named...

Claims

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Application Information

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IPC IPC(8): G06F40/295G06F40/284G06F16/36G06F40/205G06F40/242G06F16/335G06F16/35G06N3/04G06N3/08
CPCG06F40/295G06F40/284G06F40/242G06F40/205G06F16/367G06F16/335G06F16/355G06N3/08G06N3/044
Inventor 闫伟宋澳东张亮姜新泉隋远褚力宁胡晴
Owner SHANDONG NORMAL UNIV
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