Chinese Electronic Resume Named Entity Recognition Method, Device, Equipment and Storage Medium

Abstract

The present invention relates to the field of recognition technology, and discloses a method, device, equipment and storage medium for identifying named entities in Chinese electronic resumes. The method includes: obtaining an electronic resume to be identified, performing text extraction on the electronic resume to obtain text to be identified; preprocessing the text to be identified to obtain a target text; identifying the named entities of the target text based on a pre-constructed recognition model, where the recognition model is constructed based on a multi-layer attention mechanism; and visually displaying the recognition result. The present invention performs text extraction on the electronic resume to obtain text to be identified, preprocesses the text to be identified to obtain a target text, and then uses a recognition model constructed by a multi-layer attention mechanism to identify the named entities of the target text, which can capture the association between the entity semantics of the resume and the context, and improve the accuracy of named entity recognition in electronic resumes.

Description

Technical Field

[0001] The present invention relates to the field of recognition technology, and particularly to a method, device, equipment and storage medium for named entity recognition of Chinese electronic resumes. Background Art

[0002] With the rapid development of digital recruitment and talent management, as the core information carrier between job seekers and employers, the need for structured parsing of electronic resumes is becoming increasingly urgent. Named Entity Recognition (NER) technology aims to extract predefined key entities (such as names of people, organizations, time, etc.) from unstructured text, and the NER task of Chinese electronic resumes faces multiple challenges due to its unique language characteristics and application scenarios.

[0003] Chinese resume texts lack explicit delimiters (such as spaces) and contain a large number of domain-specific vocabulary (such as "Java Development Engineer", "National Encouragement Scholarship"), and need to handle word segmentation ambiguity, nested entities, and non-standard expressions (such as the mixed use of "2020.09 - 2023.06" and "from September 2020 to June 2023").

[0004] Moreover, Chinese resume NER mainly adopts rule and dictionary-driven methods, which quickly locate fixed-pattern entities based on regular expressions (such as matching phone numbers and email addresses) and domain dictionaries (such as college lists, job title libraries), but are sensitive to out-of-vocabulary words and complex contexts, resulting in low recognition accuracy. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, equipment and storage medium for named entity recognition of Chinese electronic resumes, so as to solve the problem that the existing method quickly locates fixed-pattern entities based on regular expressions and domain dictionaries, but is sensitive to out-of-vocabulary words and complex contexts, resulting in low recognition accuracy.

[0006] To achieve the above-mentioned invention purpose, the technical solution adopted by the present invention is as follows:

[0007] In the first aspect, the present invention provides a method for named entity recognition of Chinese electronic resumes, and the method includes:

[0008] Obtain an electronic resume to be recognized, perform text extraction on the electronic resume to obtain text to be recognized;

[0009] Preprocess the text to be recognized to obtain a target text;

[0010] Based on a pre-constructed recognition model, recognize the named entities of the target text to obtain a recognition result, wherein the recognition model is constructed based on a multi-layer attention mechanism;

[0011] Visualize the recognition results.

[0012] Preferably, the recognition model includes: a multi-knowledge feature extraction layer, a multi-knowledge attention layer, and a label prediction layer;

[0013] The multi-knowledge feature extraction layer is used to extract features from the target text to obtain a multi-knowledge feature vector;

[0014] The multi-knowledge attention layer is used to fuse the multi-knowledge feature vectors to obtain an interactive feature representation;

[0015] The label prediction layer is used to perform label prediction on the interactive feature representation to obtain the labels corresponding to each character in the target text, and use the labels corresponding to each character in the target text as the recognition results.

[0016] Preferably, the multi-knowledge feature vector includes: a character vector, a word vector, and a glyph vector.

[0017] Preferably, the multi-knowledge feature extraction layer includes:

[0018] A character feature extraction module, used to extract characters from the target text to obtain a character vector;

[0019] A word feature extraction module, used to extract words from the target text to obtain a word vector;

[0020] A glyph feature extraction module, used to extract glyphs from the target text to obtain a glyph vector.

[0021] Preferably, the multi-knowledge attention layer includes: a first attention module, a second attention module, and a third attention module, and both the first attention module and the second attention module are connected to the third attention module.

[0022] Preferably, fusing the multi-knowledge feature vectors to obtain an interactive feature representation includes:

[0023] Construct a ternary matrix of the first attention module based on the character vector and the word vector, and construct a ternary matrix of the second attention module based on the character vector and the glyph vector;

[0024] Based on the ternary matrix of the first attention module, determine the attention output of the first attention module;

[0025] Based on the ternary matrix of the second attention module, determine the attention output of the second attention module;

[0026] Construct a ternary matrix of the third attention module based on the attention output of the first attention module and the attention output of the second attention module;

[0027] Based on the ternary matrix of the third attention module, determine the attention output of the third attention module, and use the attention output of the third attention module as the interactive feature representation.

[0028] Preferably, the ternary matrix includes: a query matrix, a key matrix, and a value matrix. The calculation steps of the attention output of the first attention module, the attention output of the second attention module, and the attention output of the third attention module all include:

[0029] Using the query matrix, the key matrix, and the value matrix as the input of the attention function, and the attention function outputs the first matrix;

[0030] Based on a preset similarity function, calculate the similarity between the first matrix and the query matrix to obtain the second matrix;

[0031] Based on a preset linear transformation function, perform transformations on the first matrix and the second matrix respectively to obtain a third matrix corresponding to the first matrix and a fourth matrix corresponding to the second matrix;

[0032] Based on a preset non-linear transformation function, perform a transformation on the fourth matrix to obtain the fifth matrix;

[0033] Based on the fifth matrix and the third matrix, obtain the attention output.

[0034] In a second aspect, the present invention provides a Chinese electronic resume named entity recognition device for implementing the above-mentioned Chinese electronic resume named entity recognition method. The device includes:

[0035] A text extraction module, configured to obtain an electronic resume to be recognized, perform text extraction on the electronic resume, and obtain the text to be recognized;

[0036] A text processing module, configured to perform preprocessing on the text to be recognized to obtain the target text;

[0037] An entity recognition module, configured to recognize the named entities of the target text based on a pre-constructed recognition model to obtain a recognition result, wherein the recognition model is constructed based on a multi-layer attention mechanism;

[0038] A recognition display module, configured to visually display the recognition result.

[0039] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned Chinese electronic resume named entity recognition method is implemented.

[0040] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Chinese electronic resume named entity recognition method is implemented.

[0041] The beneficial effects of the present invention are mainly reflected in:

[0042] The present invention extracts text from an electronic resume to obtain text to be recognized, preprocesses the text to be recognized to obtain target text, and then uses a recognition model constructed by a multi-layer attention mechanism to recognize the named entities in the target text, which can capture the association between the entity semantics of the resume and the context, and improve the accuracy of named entity recognition of the electronic resume. Description of the Drawings

[0043] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification, and are used to explain the embodiments of the present invention together with the following specific embodiments, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0044] Figure 1 is a flowchart of a Chinese electronic resume named entity recognition method provided by an embodiment of the present invention;

[0045] Figure 2 is a block diagram of a Chinese electronic resume named entity recognition device provided by an embodiment of the present invention. Specific Embodiments

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. It should be noted here that the description of these embodiment modes is used to help understand the present invention, but does not constitute a limitation to the present invention.

[0047] Embodiment 1

[0048] Figure 1 is a flowchart of a Chinese electronic resume named entity recognition method provided by an embodiment of the present invention. As Figure 1 shown, this embodiment provides a Chinese electronic resume named entity recognition method, and the method includes:

[0049] Step S10: Obtain an electronic resume to be recognized, extract text from the electronic resume to obtain text to be recognized.

[0050] In this embodiment, the electronic resume is usually in PDF format or Word format. Tools such as pdfminer, python-docx, or OCR tools (such as PaddleOCR) can be used to extract text, and the extracted text is used as the text to be recognized.

[0051] Among them, pdfminer is an open-source tool, mainly a Python library for extracting various information from PDF documents; it can parse PDF content into text, pictures, and other metadata, even if the PDF contains complex layouts and formats.

[0052] Among them, python-docx is a Python library for creating and updating Microsoft Word (.docx) files. It allows users to create new Word documents from scratch or modify existing.docx files, including adding elements such as text, pictures, headers, footers, and tables.

[0053] Among them, OCR (Optical Character Recognition) is a recognition technology that can convert text in different types of documents (such as scanned paper documents, PDF files, or images) into a machine-editable text format.

[0054] Step S20: Preprocess the text to be recognized to obtain the target text.

[0055] In this embodiment, the preprocessing is mainly used to remove garbled characters and irrelevant characters in the text to be recognized, as well as merge broken lines and process table content.

[0056] The types of garbled characters and irrelevant characters are mainly the following types:

[0057] Encoding error characters: such as garbled characters caused by failed UTF-8 decoding;

[0058] Special symbols: irrelevant HTML tags ( ), LaTeX control characters ( ), invisible characters;

[0059] Redundant characters: consecutive repeated punctuation (------), advertising text.

[0060] Step S30: Recognize the named entities of the target text based on a pre-built recognition model to obtain the recognition result, where the recognition model is built based on a multi-layer attention mechanism.

[0061] In this embodiment, the recognition model includes: a multi-knowledge feature extraction layer, a multi-knowledge attention layer, and a label prediction layer.

[0062] Among them, the multi-knowledge feature extraction layer is used to extract features from the target text to obtain a multi-knowledge feature vector; the multi-knowledge attention layer is used to fuse the multi-knowledge feature vectors to obtain an interactive feature representation; the label prediction layer is used to perform label prediction on the interactive feature representation to obtain the labels corresponding to each character in the target text, and use the labels corresponding to each character in the target text as the recognition result.

[0063] In this embodiment, due to the limited context information of the electronic resume, the semantic information learned by the attention mechanism is limited, which will lead to poor performance of the final recognition result. Therefore, this application constructs a multi-knowledge feature extraction layer and a multi-knowledge attention layer; the multi-knowledge feature extraction layer can extract multi-knowledge features such as character vectors, word vectors, and glyph vectors, and then the multi-knowledge attention layer performs interactive fusion on the character vectors, word vectors, and glyph vectors to extract more semantic knowledge, enrich the context, and thus improve the recognition accuracy.

[0064] In this embodiment, the recognition model is trained using a sample data set and deployed after training; during the training process, data collection and labeling are performed on the electronic resume:

[0065] Label the entity types: name, contact information (phone / email), educational background (school, major, degree, time), work experience (company, position, time), skills, projects, certificates, etc.

[0066] Tool annotation: Use annotation tools (such as BRAT, Label Studio) for manual annotation, or use semi-automatic tools (such as regular expression pre-annotation).

[0067] Public data set: If there is no labeled data, Chinese resume data sets such as MSRA-NER and ResumeNER can be reused.

[0068] In this embodiment, the label prediction layer adopts the CRF (conditional random field algorithm) algorithm, and predicts the transition probability between different labels through the CRF algorithm, so as to reduce the occurrence of unreasonable label sequence combinations and improve the prediction accuracy of the recognition result.

[0069] As a further optimization of this embodiment, the multi-knowledge feature extraction layer includes: a character feature extraction module, a word feature extraction module, and a glyph feature extraction module.

[0070] Among them, the character feature extraction module is used to extract characters from the target text to obtain a character vector.

[0071] In this embodiment, the character feature extraction module uses a pre-trained BERT model to extract character knowledge. Since it is trained on a large amount of pre-trained corpora, the BERT model has learned a lot of semantic knowledge and can extract richer character semantic information. In the case of combining contexts, it effectively avoids the noise caused by character ambiguity.

[0072] Among them, the word feature extraction module is used to extract words from the target text to obtain word vectors.

[0073] In this embodiment, the word feature extraction module can adopt the Lattice-LSTM model. The Lattice-LSTM model is based on a vocabulary enhancement method and combines character and vocabulary information. When processing a sentence, it will consider all potential words in the sentence to form a structure similar to a lattice. This structure can avoid entity recognition errors caused by word segmentation errors, thereby improving the accuracy of NER.

[0074] Among them, the glyph feature extraction module is used to extract glyphs from the target text to obtain glyph vectors.

[0075] In this embodiment, since the Chinese in the target text contains glyph information, the semantics contained therein can be captured by converting the characters in the target text into corresponding Wubi codes. Therefore, the glyph feature extraction module uses an existing Wubi code conversion table to convert the characters of the target text into corresponding Wubi codes, and then uses a convolutional neural network to extract features from the Wubi codes of the target text to extract the semantic information contained in the glyphs and obtain glyph vectors.

[0076] As a further optimization of this embodiment, since there is a certain heterogeneity among characters, words, and glyphs, directly fusing the three is not conducive to exploring the complementarity between multiple knowledge. Therefore, in this embodiment, a multi-knowledge attention layer uses an attention mechanism to fuse word vectors, glyph vectors, and character vectors to strengthen the representation of the text and fully integrate context information.

[0077] In this embodiment, the multi-knowledge attention layer includes: a first attention module, a second attention module, and a third attention module. Both the first attention module and the second attention module are connected to the third attention module.

[0078] Among them, fusing the multi-knowledge feature vectors to obtain an interactive feature representation includes:

[0079] Step A1: Construct a ternary matrix of the first attention module based on the character vector and the word vector, and construct a ternary matrix of the second attention module based on the character vector and the glyph vector.

[0080] In this embodiment, the character vector and the word vector are concatenated to obtain a character-word vector, and then the character-word vector is used as the input of the first attention module. Similarly, the character vector and the glyph vector are concatenated to obtain a character-glyph vector, and then the character-glyph vector is used as the input of the second attention module.

[0081] Step A2: Determine the attention output of the first attention module based on the tri-matrix of the first attention module.

[0082] In this embodiment, the tri-matrix includes: a query matrix, a key matrix, and a value matrix. The functional expression of the tri-matrix is:

[0083] (1);

[0084] In formula (1), Q is the query matrix, K is the key matrix, V is the value matrix, X is the input vector, which is the input of the first attention module, the input of the second attention module, and the input of the third attention module in the following text; is the weight matrix of the query, is the weight matrix of the key, is the weight matrix of the value; where , and can be learned.

[0085] In this embodiment, the word vector and the character vector are fused through the first attention module, and the semantic information of the words in the given sentence is contained in the fused character vector as additional context information to enrich semantic knowledge.

[0086] Step A3: Determine the attention output of the second attention module based on the tri-matrix of the second attention module.

[0087] In this embodiment, the correlation between the character and the glyph is calculated through the second attention module to obtain a glyph-enhanced character feature representation.

[0088] Step A4: Construct the tri-matrix of the third attention module based on the attention output of the first attention module and the attention output of the second attention module.

[0089] In this embodiment, the attention output of the first attention module and the attention output of the second attention module are concatenated to obtain a concatenated vector, and the concatenated vector is used as the input of the third attention module. The tri-matrix of the third attention module is also calculated using formula (1).

[0090] Step A5: Determine the attention output of the third attention module based on the tri-matrix of the third attention module, and use the attention output of the third attention module as the interactive feature representation.

[0091] In this embodiment, the third attention module fuses the attention output of the first attention module and the attention output of the second attention module. At this time, the attention output of the third attention module contains the knowledge of text, words, and glyphs, can effectively fuse diverse information, realize the interaction of semantic information of multiple kinds of knowledge, can better help the model understand entity semantics and locate entity boundaries, and improve the accuracy of entity recognition.

[0092] Step S40: Visualize the recognition result.

[0093] In this embodiment, the recognition result is converted into a graph model or a table, the graph model or the table is displayed, or an entity report is generated, and the entity report is sent to the user terminal of the user (such as devices such as mobile phones and computers), and the entity report is visually displayed through the user terminal.

[0094] As a further optimization of this embodiment, since the query matrix Q of the three attention modules outputs a weighted average value, and whether the query matrix is relevant to the key matrix or the value matrix, the three attention modules will still generate a weighted average vector; therefore, when there is no correlation between the query matrix and the key matrix or the value matrix, the output of the three attention modules will mislead the result, thereby affecting the quality of the recognition result.

[0095] Therefore, the calculation steps of the attention output of the first attention module, the attention output of the second attention module, and the attention output of the third attention module all include:

[0096] Step B10: Use the query matrix, the key matrix, and the value matrix as the input of the attention function, and the attention function outputs the first matrix.

[0097] Step B20: Based on a preset similarity function, calculate the similarity between the first matrix and the query matrix to obtain the second matrix; among them, the similarity function uses the cosine similarity function.

[0098] Step B30: Based on a preset linear transformation function, transform the first matrix and the second matrix respectively to obtain a third matrix corresponding to the first matrix and a fourth matrix corresponding to the second matrix.

[0099] Among them, the function expressions of the third matrix and the fourth matrix are:

[0100] (2);

[0101] (3);

[0102] In formula (2), X1 is the first matrix, X2 is the second matrix, X3 is the third matrix, and X4 is the fourth matrix. linear () is a linear transformation function.

[0103] Step B40: Transform the fourth matrix based on a preset non-linear transformation function to obtain a fifth matrix.

[0104] Among them, the functional expression of the fifth matrix is:

[0105] (4);

[0106] In formula (4), X5 is the fifth matrix, and sigmoid() is a non-linear transformation function.

[0107] Step B50: Obtain an attention output based on the fifth matrix and the third matrix.

[0108] Among them, the functional expression of the attention output is:

[0109] (5);

[0110] In formula (6), X6 is the attention output, and W is a fusion weight matrix.

[0111] In this embodiment, during each attention calculation process, the cosine similarity function can be used to calculate the similarity between the first matrix and the query matrix. When the similarity is close to 1, it indicates that the first matrix is more similar to the query matrix. When the similarity is close to 0, it indicates that there is no association between the first matrix and the query matrix. After the fourth matrix undergoes non-linear transformation, if the similarity is close to 0 and there is no correlation between the two features, the corresponding weight can be cleared, which can better enhance the reasoning ability and further improve the quality of feature fusion.

[0112] The word features and glyph features of the present invention provide effective assistance in the entity recognition task of electronic Chinese resumes. Especially when there are more words involved in the entity, the contribution of word features is greater; when there are fewer words involved in the entity, Chinese character features can provide greater help to improve the recognition accuracy.

[0113] Therefore, the present invention extracts text from the electronic resume to obtain the text to be recognized, preprocesses the text to be recognized to obtain the target text, and then uses the recognition model constructed by the multi-layer attention mechanism to recognize the named entities of the target text, which can capture the association between the entity semantics of the resume and the context, and improve the accuracy of named entity recognition of the electronic resume.

[0114] Embodiment 2

[0115] Figure 2It is a Chinese electronic resume named entity recognition device provided by an embodiment of the present invention. As Figure 2 shown, this embodiment provides a Chinese electronic resume named entity recognition device, which is used to implement the Chinese electronic resume named entity recognition method in the first embodiment. The device includes:

[0116] A text extraction module, configured to obtain an electronic resume to be recognized, perform text extraction on the electronic resume, and obtain the text to be recognized;

[0117] A text processing module, configured to preprocess the text to be recognized to obtain a target text;

[0118] An entity recognition module, configured to recognize the named entities of the target text based on a pre-constructed recognition model, and obtain a recognition result, wherein the recognition model is constructed based on a multi-layer attention mechanism;

[0119] A recognition display module, configured to visually display the recognition result.

[0120] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the Chinese electronic resume named entity recognition method in the first embodiment is implemented.

[0121] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the Chinese electronic resume named entity recognition method in the first embodiment is implemented.

[0122] The present invention performs text extraction on the electronic resume to obtain the text to be recognized, preprocesses the text to be recognized to obtain the target text, and then uses a recognition model constructed by a multi-layer attention mechanism to recognize the named entities of the target text, which can capture the association between the entity semantics of the resume and the context, and improve the accuracy of named entity recognition of the electronic resume.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the specified functions in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the specified functions in one block or multiple blocks.

[0125] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for named entity recognition of Chinese electronic resumes, characterized in that, The method includes: Obtain an electronic resume to be recognized, perform text extraction on the electronic resume to obtain the text to be recognized; Preprocess the text to be recognized to obtain the target text; Based on a pre-constructed recognition model, recognize the named entities in the target text to obtain a recognition result, where the recognition model is constructed based on a multi-layer attention mechanism; the recognition model includes: a multi-knowledge feature extraction layer, a multi-knowledge attention layer, and a label prediction layer; The multi-knowledge feature extraction layer is used to extract features from the target text to obtain a multi-knowledge feature vector; the multi-knowledge feature vector includes: a character vector, a word vector, and a glyph vector; the multi-knowledge feature extraction layer includes: a character feature extraction module, which is used to extract characters from the target text to obtain a character vector; a word feature extraction module, which is used to extract words from the target text to obtain a word vector; a glyph feature extraction module, which is used to extract glyphs from the target text to obtain a glyph vector; The multi-knowledge attention layer includes: a first attention module, a second attention module, and a third attention module, and both the first attention module and the second attention module are connected to the third attention module; the multi-knowledge attention layer is used to: construct a triple matrix of the first attention module based on the character vector and the word vector, and construct a triple matrix of the second attention module based on the character vector and the glyph vector; based on the triple matrix of the first attention module, determine the attention output of the first attention module; based on the triple matrix of the second attention module, determine the attention output of the second attention module; construct a triple matrix of the third attention module based on the attention output of the first attention module and the attention output of the second attention module; based on the triple matrix of the third attention module, determine the attention output of the third attention module, and use the attention output of the third attention module as the interactive feature representation; The label prediction layer is used to perform label prediction on the interactive feature representation to obtain the labels corresponding to each character in the target text, and use the labels corresponding to each character in the target text as the recognition result; Visually display the recognition result.

2. The Chinese electronic resume named entity recognition method according to claim 1, wherein The triple matrix includes: a query matrix, a key matrix, and a value matrix, and the calculation steps of the attention output of the first attention module, the attention output of the second attention module, and the attention output of the third attention module all include: Use the query matrix, the key matrix, and the value matrix as the input of the attention function, and the attention function outputs a first matrix; Based on a preset similarity function, calculate the similarity between the first matrix and the query matrix to obtain a second matrix; Based on a preset linear transformation function, perform transformations on the first matrix and the second matrix respectively to obtain a third matrix corresponding to the first matrix and a fourth matrix corresponding to the second matrix; Based on a preset non-linear transformation function, perform a transformation on the fourth matrix to obtain a fifth matrix; Based on the fifth matrix and the third matrix, obtain the attention output.

3. A Chinese electronic resume named entity recognition device for implementing the Chinese electronic resume named entity recognition method described in claim 1 or 2, characterized in that, The device includes: A text extraction module, which is used to obtain an electronic resume to be recognized, perform text extraction on the electronic resume to obtain the text to be recognized; A text processing module, which is used to preprocess the text to be recognized to obtain the target text; The entity recognition module is used to recognize the named entities in the target text based on a pre-built recognition model to obtain a recognition result, wherein the recognition model is constructed based on a multi-layer attention mechanism; The recognition display module is used to visually display the recognition result.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the Chinese electronic resume named entity recognition method described in claim 1 or 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the Chinese electronic resume named entity recognition method described in claim 1 or 2.

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