CN110781658B - Resume analysis method, resume analysis device, electronic equipment and storage medium - Google Patents
Resume analysis method, resume analysis device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN110781658B CN110781658B CN201910973521.8A CN201910973521A CN110781658B CN 110781658 B CN110781658 B CN 110781658B CN 201910973521 A CN201910973521 A CN 201910973521A CN 110781658 B CN110781658 B CN 110781658B
- Authority
- CN
- China
- Prior art keywords
- entity
- resume
- candidate
- candidate core
- core entity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
Abstract
The disclosure provides a resume analysis method, a resume analysis device, electronic equipment and a storage medium, wherein the method comprises the following steps: identifying the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the technical problems of insufficient recall rate and low accuracy rate of resume analysis in the prior art are solved, and the resume is analyzed in a mode of attribute filling based on a core entity, so that the accuracy rate and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a resume analysis method, a resume analysis device, electronic equipment and a storage medium.
Background
At present, the basic method for extracting the working experience, the project experience and the education experience during the resume analysis is to firstly identify all entities appearing in the resume by named entities, and then extract relevant fields in the working experience, the project experience and the education experience information to form a experience based on a rule template.
However, the existing methods for extracting the work experience, the project experience and the education experience based on the rule templates have some problems, the expression forms of the work experience, the project experience and the education experience in the resume are quite different, all rule templates are difficult to be exhausted, and the resolution recall rate is not high enough; if some explanatory text is added in the key field context of the work experience, the project experience and the education experience, the rule template can be invalid, so that the resolution recall rate is affected; when the rule templates are used for matching, different experience types are difficult to distinguish, so that the analysis accuracy is not high; with the increase of rule templates, the difficulty and cost of maintenance and optimization upgrading are great.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art described above.
Therefore, a first object of the present disclosure is to provide a resume analysis method, which solves the technical problems of insufficient recall rate and low accuracy in resume analysis in the prior art, and analyzes the resume in a manner of filling attributes based on a core entity, so that the accuracy and robustness of resume analysis are greatly improved, and maintenance and update are easier.
A second object of the present disclosure is to provide a resume parsing device.
A third object of the present disclosure is to propose a computer device.
A fourth object of the present disclosure is to propose a non-transitory computer readable storage medium.
To achieve the above object, an embodiment of a first aspect of the present disclosure provides a resume parsing method, including:
identifying the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities;
acquiring each entity in text information corresponding to each candidate core entity;
filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity;
and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
Further, before the resume is identified by the preset named entity identification algorithm to obtain a plurality of candidate core entities, the method further includes:
and determining a core entity and a plurality of attribute fields corresponding to the core entity.
Further, the core entity is an enterprise name, and the plurality of attribute fields are a time attribute field and a job position attribute field;
filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity to generate structured data corresponding to each candidate core entity, wherein the structured data comprises:
if the candidate core entity exists in the row, and the number of the time entities is 2, filling the time entities into the time attribute field;
if the text information contains a position entity, filling the position entity into the position attribute field;
and generating the structured data corresponding to each candidate core entity according to the candidate core entity, the time attribute field and the job attribute field.
Further, the method further comprises the following steps:
if no time entity exists in the row of the candidate core entity, searching the time entity in the text information according to the row sequence, and if only the time entity exists in any row and the number of the time entities is 2, filling the time entity into the time attribute field.
Further, before the generating the resume content corresponding to the resume according to the structured data corresponding to each candidate core entity, the method further includes:
judging whether the structured data corresponding to each candidate core entity meets a preset rule or not;
if the structured data corresponding to the target candidate core entity does not meet the preset rule, deleting the structured data corresponding to the target candidate core entity.
Further, before the generating the resume content corresponding to the resume according to the structured data corresponding to each candidate core entity, the method further includes:
judging whether the structured data corresponding to each candidate core entity meets a preset merging condition or not;
if the structured data corresponding to any two candidate core entities meet the preset merging condition, merging the structured data corresponding to the two candidate core entities.
According to the resume analysis method, a resume is identified through a preset named entity identification algorithm, and a plurality of candidate core entities are obtained; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the technical problems of insufficient recall rate and low accuracy rate of resume analysis in the prior art are solved, and the resume is analyzed in a mode of attribute filling based on a core entity, so that the accuracy rate and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
To achieve the above object, an embodiment of a second aspect of the present disclosure provides a resume parsing apparatus, including:
the identifying and acquiring module is used for identifying the resume through a preset named entity identifying algorithm to acquire a plurality of candidate core entities;
the acquisition module is used for acquiring each entity in the text information corresponding to each candidate core entity;
the filling module is used for filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity to generate structured data corresponding to each candidate core entity;
and the generation module is used for generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
Further, the device further comprises:
and the determining module is used for determining the core entity and a plurality of attribute fields corresponding to the core entity.
Further, the core entity is an enterprise name, and the plurality of attribute fields are a time attribute field and a job position attribute field;
the filling module is specifically configured to:
if the candidate core entity exists in the row, and the number of the time entities is 2, filling the time entities into the time attribute field;
if the text information contains a position entity, filling the position entity into the position attribute field;
and generating the structured data corresponding to each candidate core entity according to the candidate core entity, the time attribute field and the job attribute field.
Further, the filling module is specifically further configured to:
if no time entity exists in the row of the candidate core entity, searching the time entity in the text information according to the row sequence, and if only the time entity exists in any row and the number of the time entities is 2, filling the time entity into the time attribute field.
Further, the device further comprises:
the first judging module is used for judging whether the structured data corresponding to each candidate core entity meets a preset rule or not;
and the deleting module is used for deleting the structured data corresponding to the target candidate core entity if the structured data corresponding to the target candidate core entity does not meet the preset rule.
Further, the device further comprises:
the second judging module is used for judging whether the structured data corresponding to each candidate core entity meets the preset merging condition or not;
and the merging module is used for merging the structured data corresponding to any two candidate core entities if the structured data corresponding to any two candidate core entities meet the preset merging condition.
The resume analysis device of the embodiment of the disclosure identifies the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the technical problems of insufficient recall rate and low accuracy rate of resume analysis in the prior art are solved, and the resume is analyzed in a mode of attribute filling based on a core entity, so that the accuracy rate and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
To achieve the above object, an embodiment of a third aspect of the present disclosure provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the resume parsing method as described above when executing the program.
In order to achieve the above object, a fourth aspect of the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the resume parsing method as described above.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a resume parsing method according to one embodiment of the present disclosure;
FIG. 2 is a flow chart of a resume parsing method according to another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a resume parsing device according to one embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a resume parsing device according to another embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a resume parsing device according to yet another embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a resume parsing apparatus according to still another embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
The following describes a resume parsing method, apparatus, electronic device, and storage medium of an embodiment of the present disclosure with reference to the accompanying drawings.
Aiming at the technical problems that the recall rate and the accuracy rate of resume analysis are not high enough in the prior art and the background art, the disclosure provides a resume analysis method, which is used for identifying a resume through a preset named entity identification algorithm to acquire a plurality of candidate core entities; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the resume is analyzed in a mode of attribute filling based on the core entity, so that the accuracy and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
Specifically, fig. 1 is a flowchart of a resume parsing method according to an embodiment of the present disclosure, as shown in fig. 1, the method includes:
and step 101, identifying the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities.
Specifically, the core entity may be predetermined according to the actual application requirement, that is, the key information to be acquired may be, for example, a business name, a school name, etc. as the core entity, and a plurality of attribute fields corresponding to the core entity, for example, a time attribute field, a job position attribute field, a professional attribute field, etc. may be set.
Further, the resume is identified through a preset named entity identification algorithm to obtain a plurality of candidate core entities, for example, the resume is identified through a machine learning model or rule, and the plurality of candidate core entities are obtained to be enterprise names and school names.
Step 102, obtaining each entity in the text information corresponding to each candidate core entity.
It may be understood that each candidate core entity has its corresponding text information in the resume, for example, the text information between two core entities is the text information corresponding to the last core entity, and for example, there is no core entity behind one core entity, then the text information behind the core entity is considered to be the text information corresponding to the core entity.
It can be appreciated that the entity identification can be performed on the text information corresponding to each candidate core entity to obtain each entity, such as a time entity, a job entity, a professional entity, and the like.
And step 103, filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity.
And 104, generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
Specifically, after each entity in the text information corresponding to each candidate core entity is obtained, filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, generating structured data corresponding to each candidate core entity, and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
As one possible implementation manner, the core entity is an enterprise name, the plurality of attribute fields are a time attribute field and a position attribute field, if a time entity exists in a row where the candidate core entity is located and the number of the time entities is 2, the time entity is filled in the time attribute field, if a position entity exists in the text information, the position entity is filled in the position attribute field, and structured data corresponding to the enterprise name is generated according to the candidate core entity, the time attribute field and the position attribute field.
As another possible implementation manner, the core entity is a school name, the plurality of attribute fields are a time attribute field and a professional attribute field, if no time entity exists in a row where the candidate core entity exists, the time entity is searched in the text information according to the row sequence, if only the time entity exists in any row and the number of the time entities is 2, the time entity is filled in the time attribute field, if the professional entity exists in the text information, the professional entity is filled in the professional attribute field, and structured data corresponding to the school name is generated according to the candidate core entity, the time attribute field and the professional attribute field.
In summary, according to the resume analysis method of the embodiment of the disclosure, a resume is identified through a preset named entity identification algorithm, and a plurality of candidate core entities are obtained; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the technical problems of insufficient recall rate and low accuracy rate of resume analysis in the prior art are solved, and the resume is analyzed in a mode of attribute filling based on a core entity, so that the accuracy rate and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
Fig. 2 is a flowchart of a resume parsing method according to another embodiment of the present disclosure, as shown in fig. 2, the method including:
step 201, determining a core entity and a plurality of attribute fields corresponding to the core entity, and identifying the resume through a preset named entity identification algorithm to obtain a plurality of candidate enterprise names.
Specifically, taking a working experience as an example, a key-value structure may be defined as follows, a company is a name of a business involved in the working, a start_time is a time of joining the company, an end_time is a time of leaving the company, a position is a role of the job, a core entity of a experience is defined according to characteristics of each field, for example, the working experience may define a name field of the business as the core entity, and other fields are defined as attribute fields of the experience, for example, start_time and end_ time, position are attribute fields of the working experience. { "company": "xx company", "start_time":20xx-xx-xx, "end_time":20xx-xx-xx, "position": "xx engineer" }.
Specifically, the entities in the resume text are identified by a named entity recognition algorithm, and the candidate core entities are positioned by a machine learning model or rule so that each entity identified as a business name serves as a core entity of the potential work experience.
Step 202, obtaining each entity in the text information corresponding to each candidate enterprise name, and if the candidate enterprise names are in the presence of time entities in the row and the number of the time entities is 2, filling the time entities into the time attribute field.
In step 203, if the candidate business names are in the rows and there is no time entity, the time entity is searched in the text information according to the row sequence, and if only the time entity exists in any row and the number of the time entities is 2, the time entity is filled in the time attribute field.
Specifically, the time attribute field of the candidate business name is filled in: since a working experience involves two times (start_time and end_time), the time is found first from the core entity context information, for example, defining a set of career_time_lines, if a time entity in a certain line already belongs to a certain candidate business name, adding the line number of the line into the set of career_time_lines, if the number of elements in the candidate time attribute list is equal to 2, taking the smaller time as the start_time and the larger time as the end_time, and ending; or the core entity currently runs with a time entity and has no other core entities, if the line number is not in the career_time_lines, adding the time entity into a candidate time attribute list, adding the line number into a career_time_lines set, and if the number of elements in the candidate time attribute list is equal to 2, taking smaller time as start_time and larger time as end_time, and ending; or any line of the text information corresponding to the core entity, wherein the line is provided with a time entity and no other core entity compared with the first line of the reciprocal of the graph, the line number is not in the career_time_lines, the time entity is added into a candidate time attribute list, if the number of elements in the candidate time attribute list is equal to 2, the smaller time is used as start_time, the larger time is used as end_time, and the process is finished.
And 204, if the position entity exists in the text information, filling the position entity into the position attribute field.
Specifically, since there is only one job attribute field in one job experience, it is only necessary to search and fill the job attribute field in the text information corresponding to the core entity, i.e. the context.
Step 205, generating structured data corresponding to each enterprise name according to the candidate enterprise names, the time attribute field and the job attribute field.
Step 206, judging whether the structured data corresponding to each candidate enterprise name meets the preset rule, if the structured data corresponding to the target candidate enterprise name does not meet the preset rule, deleting the structured data corresponding to the target candidate enterprise name.
Step 207, determining whether the structured data corresponding to each candidate enterprise name meets a preset merging condition, and if any two structured data corresponding to the candidate enterprise names meet the preset merging condition, merging the structured data corresponding to the two candidate enterprise names.
And step 208, generating resume content corresponding to the resume according to the structured data corresponding to each candidate enterprise name.
Specifically, whether each candidate enterprise name is legal or not is judged through a certain rule, if the candidate enterprise name is required to have a start_time, end_time or position attribute, the candidate enterprise name which does not meet the legal requirement is discarded, and the specific legal rule can be flexibly customized based on the experience type and the service requirement.
Specifically, if multiple candidate business names are the same, and both start_time and end_time are the same, then it is considered duplicative, and duplicate candidate business names are discarded.
Specifically, if two candidate enterprises have the same names and one of the two candidate enterprises has the same start_time and the other one of the two candidate enterprises has the same end_time, the two candidate enterprises are described as continuous working experiences of the same enterprise, and the two candidate enterprises are combined into a complete working experience. And when merging, taking the earliest working time as the start_time, taking the latest ending time as the end_time, merging the positions into a list, and obtaining the merged candidate working experience as the final analysis working experience content.
After each entity in the text information corresponding to each candidate core entity is obtained, filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity to generate structured data corresponding to each candidate core entity, and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
In summary, the resume parsing method of the embodiment of the present disclosure determines a core entity and a plurality of attribute fields corresponding to the core entity, identifies the resume through a preset named entity identification algorithm, obtains a plurality of candidate business names, obtains each entity in text information corresponding to each candidate business name, if the candidate business names exist in a presence time entity and the number of the time entities is 2, fills the time entity into the time attribute field, if the candidate business names do not exist in the presence time entity, searches the time entity in the text information according to a line sequence, if only the time entity exists in any line and the number of the time entity is 2, fills the time entity into the time attribute field, if the job entity exists in the text information, fills the job attribute field with the candidate entity, generates structured data corresponding to each candidate business name according to the candidate business name, the time attribute field and the job attribute field, judges whether the structured data corresponding to each candidate business name meets a preset rule, if the structured data corresponding to the candidate name does not meet the preset rule, merges the two corresponding structured data, and if the two corresponding structured data corresponding to the candidate name are merged, then performs the merging the corresponding structured data according to the preset rule, and the method is flexible when the core entity context is filled with the entity, so that the resolution accuracy is greatly improved, and because the template is not used for matching during resolution, maintenance and optimization upgrading are easier.
Fig. 3 is a schematic structural diagram of a resume parsing device according to an embodiment of the present disclosure. As shown in fig. 3, includes: an identification acquisition module 301, an acquisition module 302, a population module 303, and a generation module 304.
The identifying and acquiring module 301 is configured to identify the resume through a preset named entity identifying algorithm, and acquire a plurality of candidate core entities.
And the acquiring module 302 is configured to acquire each entity in the text information corresponding to each candidate core entity.
And a filling module 303, configured to fill, according to each entity in the text information corresponding to each candidate core entity, a plurality of attribute fields corresponding to each candidate core entity, and generate structured data corresponding to each candidate core entity.
And the generating module 304 is configured to generate resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
In one embodiment of the present disclosure, as shown in fig. 4, the apparatus further comprises: a determination module 305.
The determining module 305 is configured to determine a core entity and a plurality of attribute fields corresponding to the core entity.
In one embodiment of the present disclosure, the core entity is an enterprise name, and the plurality of attribute fields are a time attribute field and a job attribute field; the filling module 303 is specifically configured to: if the candidate core entity exists in the row, and the number of the time entities is 2, filling the time entities into the time attribute field; if the text information contains a position entity, filling the position entity into the position attribute field; and generating the structured data corresponding to each candidate core entity according to the candidate core entity, the time attribute field and the job attribute field.
In one embodiment of the present disclosure, the filling module 303 is specifically further configured to: if no time entity exists in the row of the candidate core entity, searching the time entity in the text information according to the row sequence, and if only the time entity exists in any row and the number of the time entities is 2, filling the time entity into the time attribute field.
Further, on the basis of the above embodiment, as shown in fig. 5, the apparatus further includes: a first determination module 306 and a deletion module 307.
A first determining module 306, configured to determine whether the structured data corresponding to each candidate core entity meets a preset rule;
and the deleting module 307 is configured to delete the structured data corresponding to the target candidate core entity if the structured data corresponding to the target candidate core entity does not meet the preset rule.
Further, on the basis of the above embodiment, as shown in fig. 6, the apparatus further includes: a second determination module 308 and a combination module 309.
A second determining module 308, configured to determine whether the structured data corresponding to each candidate core entity meets a preset merging condition.
And a merging module 309, configured to, if any two of the structured data corresponding to the candidate core entities satisfy the preset merging condition, merge the structured data corresponding to the two candidate core entities.
The resume analysis device of the embodiment of the disclosure identifies the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities; acquiring each entity in text information corresponding to each candidate core entity; filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity; and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity. Therefore, the technical problems of insufficient recall rate and low accuracy rate of resume analysis in the prior art are solved, and the resume is analyzed in a mode of attribute filling based on a core entity, so that the accuracy rate and the robustness of resume analysis are greatly improved, and the resume is easier to maintain and update.
Referring now to fig. 7, a schematic diagram of an electronic device 900 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 7, the electronic device 900 may include a processing means (e.g., a central processor, a graphics processor, etc.) 901, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage means 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
In general, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device 900 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 shows an electronic device 900 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 909, or installed from the storage device 908, or installed from the ROM 902. When executed by the processing device 901, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a page rendering request, wherein the page rendering request comprises the following steps: the selected region in the page, and the rendering type; the region includes: at least one cell, and/or text data; if the area includes at least one cell, acquiring an identifier of the at least one cell, where the identifier of the cell includes: a row identifier and a column identifier; and according to the rendering type, rendering the cell corresponding to the identification of the at least one cell.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The present disclosure also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a resume parsing method as described above.
The present disclosure also provides a computer program product which, when executed by an instruction processor in the computer program product, implements a resume parsing method as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.
Claims (9)
1. The resume analysis method is characterized by comprising the following steps of:
a core entity and a plurality of attribute fields corresponding to the core entity are predetermined according to actual application requirements; wherein the core entity comprises a business name and a school name;
identifying the resume through a preset named entity identification algorithm to obtain a plurality of candidate core entities; wherein the candidate core entity corresponds to the core entity;
acquiring each entity in text information corresponding to each candidate core entity; wherein each entity comprises a time entity, a position entity and a professional entity;
filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity;
and generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
2. The method of claim 1, wherein the core entity is a business name and the plurality of attribute fields are a time attribute field and a job attribute field;
filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity to generate structured data corresponding to each candidate core entity, wherein the structured data comprises:
if the candidate core entity exists in the row, and the number of the time entities is 2, filling the time entities into the time attribute field;
if the text information contains a position entity, filling the position entity into the position attribute field;
and generating the structured data corresponding to each candidate core entity according to the candidate core entity, the time attribute field and the job attribute field.
3. The method as recited in claim 2, further comprising:
if no time entity exists in the row of the candidate core entity, searching the time entity in the text information according to the row sequence, and if only the time entity exists in any row and the number of the time entities is 2, filling the time entity into the time attribute field.
4. The method of claim 1, further comprising, prior to said generating resume content corresponding to said resume from structured data corresponding to said each candidate core entity:
judging whether the structured data corresponding to each candidate core entity meets a preset rule or not;
if the structured data corresponding to the target candidate core entity does not meet the preset rule, deleting the structured data corresponding to the target candidate core entity.
5. The method of claim 1, further comprising, prior to said generating resume content corresponding to said resume from structured data corresponding to said each candidate core entity:
judging whether the structured data corresponding to each candidate core entity meets a preset merging condition or not;
if the structured data corresponding to any two candidate core entities meet the preset merging condition, merging the structured data corresponding to the two candidate core entities.
6. A resume analysis device, comprising:
the setting module is used for presetting a core entity and a plurality of attribute fields corresponding to the core entity according to actual application requirements; wherein the core entity comprises a business name and a school name;
the identifying and acquiring module is used for identifying the resume through a preset named entity identifying algorithm to acquire a plurality of candidate core entities; wherein the candidate core entity corresponds to the core entity;
the acquisition module is used for acquiring each entity in the text information corresponding to each candidate core entity; wherein each entity comprises a time entity, a position entity and a professional entity;
the filling module is used for filling a plurality of attribute fields corresponding to each candidate core entity according to each entity in the text information corresponding to each candidate core entity, and generating structured data corresponding to each candidate core entity;
and the generation module is used for generating resume content corresponding to the resume according to the structured data corresponding to each candidate core entity.
7. The apparatus as recited in claim 6, further comprising:
the first judging module is used for judging whether the structured data corresponding to each candidate core entity meets a preset rule or not;
and the deleting module is used for deleting the structured data corresponding to the target candidate core entity if the structured data corresponding to the target candidate core entity does not meet the preset rule.
8. 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 resume parsing method of any of claims 1-5 when the computer program is executed by the processor.
9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the resume parsing method according to any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910973521.8A CN110781658B (en) | 2019-10-14 | 2019-10-14 | Resume analysis method, resume analysis device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910973521.8A CN110781658B (en) | 2019-10-14 | 2019-10-14 | Resume analysis method, resume analysis device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110781658A CN110781658A (en) | 2020-02-11 |
| CN110781658B true CN110781658B (en) | 2023-08-25 |
Family
ID=69386166
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910973521.8A Active CN110781658B (en) | 2019-10-14 | 2019-10-14 | Resume analysis method, resume analysis device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110781658B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325031B (en) * | 2020-02-17 | 2023-06-23 | 抖音视界有限公司 | Resume analysis method and device |
| CN111352979B (en) * | 2020-03-31 | 2024-01-12 | 中国建设银行股份有限公司 | Industry analysis method and system based on resume information |
| CN111460084A (en) * | 2020-04-03 | 2020-07-28 | 中国建设银行股份有限公司 | Resume structured extraction model training method and system |
| CN112214572B (en) * | 2020-10-20 | 2022-11-01 | 山东浪潮科学研究院有限公司 | Method for secondarily extracting entities in resume analysis |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2503477A1 (en) * | 2011-03-21 | 2012-09-26 | Tata Consultancy Services Limited | A system and method for contextual resume search and retrieval based on information derived from the resume repository |
| WO2016045153A1 (en) * | 2014-09-25 | 2016-03-31 | 中国科学院软件研究所 | Information visualization method and intelligent visible analysis system based on textual resume information |
| US10146751B1 (en) * | 2014-12-31 | 2018-12-04 | Guangsheng Zhang | Methods for information extraction, search, and structured representation of text data |
| CN109754233A (en) * | 2019-01-29 | 2019-05-14 | 上海嘉道信息技术有限公司 | A kind of method and system of intelligent recommendation job information |
| CN109766438A (en) * | 2018-12-12 | 2019-05-17 | 平安科技（深圳）有限公司 | Biographic information extracting method, device, computer equipment and storage medium |
| CN109948120A (en) * | 2019-04-02 | 2019-06-28 | 深圳市前海欢雀科技有限公司 | A kind of resume analytic method based on dualization |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8473503B2 (en) * | 2011-07-13 | 2013-06-25 | Linkedin Corporation | Method and system for semantic search against a document collection |
| US20130085954A1 (en) * | 2011-09-29 | 2013-04-04 | The Boeing Company | System and method for identifying a qualified candidate |
| US10643182B2 (en) * | 2016-03-16 | 2020-05-05 | Oracle International Corporation | Resume extraction based on a resume type |
-
2019
- 2019-10-14 CN CN201910973521.8A patent/CN110781658B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2503477A1 (en) * | 2011-03-21 | 2012-09-26 | Tata Consultancy Services Limited | A system and method for contextual resume search and retrieval based on information derived from the resume repository |
| WO2016045153A1 (en) * | 2014-09-25 | 2016-03-31 | 中国科学院软件研究所 | Information visualization method and intelligent visible analysis system based on textual resume information |
| US10146751B1 (en) * | 2014-12-31 | 2018-12-04 | Guangsheng Zhang | Methods for information extraction, search, and structured representation of text data |
| CN109766438A (en) * | 2018-12-12 | 2019-05-17 | 平安科技（深圳）有限公司 | Biographic information extracting method, device, computer equipment and storage medium |
| CN109754233A (en) * | 2019-01-29 | 2019-05-14 | 上海嘉道信息技术有限公司 | A kind of method and system of intelligent recommendation job information |
| CN109948120A (en) * | 2019-04-02 | 2019-06-28 | 深圳市前海欢雀科技有限公司 | A kind of resume analytic method based on dualization |
Non-Patent Citations (1)
| Title |
|---|
| 基于字序列的非结构化简历信息解析方法;陈毅;符磊;张剑;黄石磊;;计算机工程与设计(06);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110781658A (en) | 2020-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110781658B (en) | Resume analysis method, resume analysis device, electronic equipment and storage medium | |
| CN107943877B (en) | Method and device for generating multimedia content to be played | |
| CN109726390B (en) | Document processing method, device, electronic equipment and storage medium | |
| CN112395253B (en) | Index file generation method, terminal device, electronic device and medium | |
| WO2022111591A1 (en) | Page generation method and apparatus, storage medium, and electronic device | |
| CN111857720B (en) | User interface state information generation method and device, electronic equipment and medium | |
| CN111738316B (en) | Zero sample learning image classification method and device and electronic equipment | |
| CN111309228A (en) | Multimedia processing method and device and electronic equipment | |
| CN111552640A (en) | Code detection method, device, equipment and storage medium | |
| CN112115153A (en) | Data processing method, device, equipment and storage medium | |
| CN111325031B (en) | Resume analysis method and device | |
| CN111367516A (en) | Application interface generation method and device and electronic equipment | |
| CN109947431B (en) | Code generation method, device, equipment and storage medium | |
| CN109902726B (en) | Resume information processing method and device | |
| CN111241823A (en) | Dependency configuration management method and device, electronic equipment and storage medium | |
| CN111107133A (en) | Generation method of difference packet, data updating method, device and storage medium | |
| CN111124541B (en) | Configuration file generation method, device, equipment and medium | |
| CN114995691B (en) | Document processing method, device, equipment and medium | |
| CN111641690B (en) | Session message processing method and device and electronic equipment | |
| CN111787043A (en) | Data request method and device | |
| CN109857838B (en) | Method and apparatus for generating information | |
| CN116185412B (en) | Data management method and device, electronic equipment and storage medium | |
| CN116663529A (en) | Entry generation method and device and electronic equipment | |
| CN116129324A (en) | Special effect identification method, device, medium and electronic equipment | |
| CN116204740A (en) | Label determining method, information recommending method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Applicant after: Tiktok vision (Beijing) Co.,Ltd. Address before: Room B0035, 2nd floor, No. 3 Courtyard, 30 Shixing Street, Shijingshan District, Beijing, 100041 Applicant before: BEIJING BYTEDANCE NETWORK TECHNOLOGY Co.,Ltd. Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Applicant after: Douyin Vision Co.,Ltd. Address before: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Applicant before: Tiktok vision (Beijing) Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |