CN111198943A - Resume screening method and device and terminal equipment - Google Patents
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Abstract
The invention is suitable for the technical field of information processing, and provides a resume screening method, a resume screening device and terminal equipment, wherein the resume screening method comprises the following steps: acquiring resume information of an applicant to obtain a first resume; extracting keywords of the first resume to obtain a second resume with the keywords; classifying the second resume according to a preset recruitment position, wherein the number of the preset recruitment positions is two or more; and screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value. By the method, simultaneous recruitment of multiple positions can be realized.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a resume screening method and device and terminal equipment.
Background
With the development of internet technology, more and more talent recruits are realized through recruiting websites. Usually, the recruiters post resumes on the recruitment website, and the recruitment website performs preliminary screening on the resumes of the recruiters according to screening conditions set by the enterprise, so that the workload of the recruiters is reduced.
However, the conventional resume screening system for the recruitment website can only screen resumes of one job at a time, and the requirement of simultaneous recruitment of multiple jobs of an enterprise is difficult to meet.
Disclosure of Invention
In view of this, embodiments of the present invention provide a resume screening method, an apparatus, and a terminal device, so as to solve a problem in the prior art that simultaneous recruitment for multiple positions cannot be realized during network resume screening.
A first aspect of an embodiment of the present invention provides a resume screening method, including:
acquiring resume information of an applicant to obtain a first resume;
extracting keywords of the first resume to obtain a second resume with the keywords;
classifying the second resume according to a preset recruitment position, wherein the number of the preset recruitment positions is two or more;
and screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value.
A second aspect of an embodiment of the present invention provides a resume screening apparatus, including:
the data acquisition unit is used for acquiring resume information of an applicant to obtain a first resume;
the keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords;
the resume classification evaluation unit is used for classifying the second resume according to a preset recruitment position and evaluating scores, wherein the number of the preset recruitment positions is two or more;
and the resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the resume screening method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the resume screening method as described.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the resumes are classified and the scores are evaluated according to the preset plurality of recruitment positions after keywords are extracted, so that all resumes with the scores higher than a preset screening threshold value in the plurality of recruitment positions can be screened from the resume database at one time, the simultaneous recruitment of multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a first resume screening method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a second resume screening method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a resume screening apparatus according to an embodiment of the present application;
fig. 4 is a schematic diagram of a terminal device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In addition, in the description of the present application, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
The first embodiment is as follows:
fig. 1 shows a schematic flow chart of a first resume screening method provided in an embodiment of the present application, which is detailed as follows:
in S101, resume information of an applicant is collected to obtain a first resume.
The resume information of the applicant can be collected according to resume data filled in by the applicant on a website or an electronic resume directly delivered by the applicant on the internet, so that the first resume is obtained and stored in the resume database.
The first resume can include at least one of the name, sex, academic record, graduation colleges, specialties, achievements, community experience, social work experience and the like of the applicant.
In this embodiment, resume information of an applicant is collected according to a collection range set by a user, where the collection range may be a website range corresponding to different websites, or a range corresponding to different pages in the same website.
In S102, the keyword of the first resume is extracted to obtain a second resume with the keyword.
In order to quickly and accurately screen out the required resume, keyword extraction is carried out on each first resume in the resume database to obtain a second resume with keywords, so that subsequent resume screening is facilitated. The number of the keywords is plural, and the keywords may include words describing information such as a academic calendar, a college, a discipline specialty, and an english level. For example, the keywords describing the academic calendar may be elementary school, junior high school, this subject, student, doctor, etc.
In S103, the second resumes are classified according to preset recruitment positions, and scores are evaluated, wherein the number of the preset recruitment positions is two or more.
And classifying the second resume with the keywords according to the preset recruitment positions, firstly obtaining the classification probability of the second resume being classified into each preset recruitment position, and finally classifying the second resume into the preset recruitment position with the highest corresponding classification probability. And when the classification probabilities of the second resume classified into the preset recruitment positions are the same and the second resume is the highest, distributing the second resume to the preset recruitment positions at the same time, namely, the second resume corresponds to a plurality of recruitment positions. And after the classification, evaluating and scoring the second resumes in the preset recruitment positions assigned with the second resumes respectively to obtain second resume scores S in the preset recruitment positions. Wherein, the number of the preset recruitment positions is two or more, and the resumes can be classified.
In S104, the corresponding first resume is screened according to the evaluation score of the second resume and a preset screening threshold.
And screening the corresponding first resume from the resume database according to the evaluation score S of the second resume in the divided preset recruitment positions and the preset screening threshold F corresponding to the recruitment positions. And if the S is larger than or equal to the F, extracting the first resume corresponding to the second resume from the resume database, otherwise, not extracting the resume, and finally screening the first resume corresponding to each preset recruitment position from the resume database.
The first resume corresponding to the second resume refers to the first resume before the keyword is extracted from the second resume, that is, the second resume is obtained by extracting the keyword from the corresponding first resume. Specifically, a resume number ID is set in the first resume, a second resume is obtained by extracting keywords of the first resume, the resume number ID of the first resume is copied to the second resume, so that the first resume and the second resume with the same resume number ID correspond to each other one by one, and then the first resume with the same resume number ID can be extracted according to the resume number ID of the second resume.
In the embodiment of the invention, the resumes are classified and evaluated according to the preset plurality of recruitment positions after the keywords of the resumes are extracted, so that all resumes with scores higher than a preset screening threshold value in the plurality of recruitment positions can be screened from the resume database at one time, the simultaneous recruitment of multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
Example two:
fig. 2 shows a schematic flow chart of a second resume screening method provided in the embodiment of the present application, which is detailed as follows:
in S201, resume information of an applicant is collected to obtain a first resume.
In this embodiment, S201 is the same as S101 in the previous embodiment, and please refer to the related description of S101 in the first embodiment, which is not described herein again.
In S202, the keyword of the first resume is extracted to obtain a second resume with the keyword.
In order to quickly and accurately screen out the required resume, keyword extraction is carried out on each first resume in the resume database to obtain a second resume with keywords, so that subsequent resume screening is facilitated.
Optionally, the keywords of the first resume are extracted through a pre-trained keyword extraction model, so as to obtain a second resume with the keywords.
Specifically, a keyword extraction model is trained in advance through a deep learning algorithm and used for extracting keywords of the first resume, and the deep learning algorithm can acquire more accurate information, so that when the keywords of the first resume are extracted through the keyword extraction model obtained through training of the deep learning algorithm, effective information in the first resume can be extracted more accurately and efficiently, and a second resume only with keyword information concerned by enterprises is obtained.
Optionally, the pre-trained keyword extraction model specifically is a model for extracting keywords trained by using a keyword library as a label and a preset resume as a sample.
In the model training stage, a keyword library is firstly established, which can comprise a academic keyword library, a university school name keyword library, a subject professional name keyword library, a university English grade keyword library and a score keyword library, a practice company name and position keyword library, a working time and working position keyword library, a college period student working position keyword library, an engaging position and salary keyword library and the like, and a plurality of keyword libraries are selected according to specific needs and packaged into a final keyword library. The plurality of keyword libraries selected here may be a combination of part or all of the above listed keyword libraries, and may include other optional keyword libraries not listed.
And after the keyword library is established, training a model which can be used for extracting the keywords by taking the finally determined keyword library as a label and taking resume information of some applicants acquired from the Internet as samples. The model extracts keywords in a Sequence-to-Sequence (Sequence to Sequence) manner. Specifically, the training process of the model comprises the following steps:
A1. and mapping each word in the first resume sentence into a word vector to obtain a matrix corresponding to the sentence.
Each word in the first resume sentence is mapped into an N-dimensional vector, the N-dimensional vector forms a word vector, the word vector is obtained by adopting a word vector algorithm word2vec (word to vector), and finally, each sentence correspondingly obtains an Embedding matrix of T x N, wherein T is the length of the sentence.
A2. And coding the Embedding matrix to obtain a coding vector.
A Long Short-Term Memory network (LSTM) with a single-layer structure is used as an Encoder Encoder, the number of 128 units is used as the unit number of the LSTM, and each Embedding matrix is encoded into a corresponding 128-dimensional encoding vector.
A3. And decoding the coded vector to obtain a decoding result.
The above-described encoded vector is input to a decoder at a time, the decoder uses the same model structure as the encoder, and the decoder outputs the decoding result.
A4. And comparing the decoding result with the keyword library label to optimize the keyword extraction model.
And comparing the decoding result with the label of the keyword library, and further correcting the decoding result, so that the probability of outputting correct keywords every time is improved, and the keyword extraction model is optimized.
In S203, receiving a setting instruction, where the setting instruction includes setting information of a recruitment position and setting information of a screening threshold; and carrying out resume classification screening according to the setting information.
Specifically, when the preset recruitment positions and the screening threshold do not exist or the preset information needs to be changed, a setting instruction is received, the recruitment positions and the corresponding screening threshold are set according to the current recruitment need, wherein the number of the set recruitment positions is multiple, and each recruitment position is correspondingly provided with one screening threshold. And after the setting, storing the setting information as the preset recruitment position and the corresponding preset screening threshold value for subsequent resume classification screening.
In S204, the second resumes are classified according to preset recruitment positions, and scores are evaluated, wherein the number of the preset recruitment positions is two or more.
And classifying the second resumes with the keywords according to the preset recruitment positions, wherein the number of the preset recruitment positions is two or more, and evaluating the score of the second resumes classified to the recruitment positions. Wherein, the number of the preset recruitment positions is two or more, and the resumes can be classified.
Optionally, in step S204, specifically, the keyword of the first resume is extracted through a pre-trained keyword extraction model, so as to obtain a second resume with the keyword.
Specifically, the method comprises the following two steps:
and S204.1, classifying the second resume according to the preset recruitment position through a pre-trained text classification evaluation model, and simultaneously returning the classification probability of the second resume to the preset recruitment position.
Specifically, the step S204.1 includes:
and S204.1B1, obtaining a corresponding word vector Embedding matrix of the second resume only with the keyword information through a word2vec algorithm, wherein the dimension of the matrix is T x N, T is the sentence length, and N is the dimension of each word.
And S204.1B2, performing convolution operation on the Embbeding matrix by using a convolution layer containing M convolution kernels with the size of H x N to obtain a corresponding feature map. Wherein, M is the number of convolution kernel channels, preferably 9, one channel represents one convolution kernel, and one convolution kernel corresponds to one feature map; h is the length of one side of the convolution kernel, preferably 3, and the length of the other side of the convolution kernel is equal to the length N of the sentence.
S204.1B3, obtaining the maximum value max _ value in each feature map through the maximum pooling layer max-pool, and performing cascade operation on all max _ values to obtain a matrix of 1M.
And S204.1B4, according to the matrix of 1 x M and the preset recruitment positions, judging the content of the matrix by utilizing a softmax multi-classifier to obtain the classification probability of the corresponding second resume to each preset recruitment position, judging the preset recruitment position to which the second resume is classified to have higher classification probability, finally classifying the second resume to the preset recruitment position with the highest classification probability, and returning the classification probability of the second resume classified to the preset recruitment position. And when the classification probabilities of the second resume classified into the preset recruitment positions are the same and the second resume is the highest, distributing the second resume to the preset recruitment positions at the same time, namely, the second resume corresponds to a plurality of recruitment positions.
And S204.2, determining the evaluation score of the second resume according to the classification probability.
And determining the evaluation score S of the second resume according to the classification probability P returned when the second resume is classified to the corresponding preset recruitment position, wherein S is P100.
In S205, the corresponding first resume is filtered according to the evaluation score of the second resume and a preset filtering threshold.
In this embodiment, S205 is the same as S104 in the previous embodiment, and please refer to the related description of S104 in the first embodiment, which is not repeated herein.
Optionally, after step S205, the method further includes: and sending the screened first resume.
And sending the resume screened from the resume database by the first resume to the enterprise, and sending the screened resume to the recruitment department through prestored mailbox information of the enterprise recruitment department so as to inform the recruitment personnel of the enterprise in time. Specifically, a time period can be preset, and the first resumes corresponding to each of the screened preset recruitment positions are respectively packaged and sent to the enterprise at intervals, so that the enterprise can perform subsequent recruitment work conveniently.
According to the embodiment of the invention, the recruitment positions and the screening threshold are set according to the recruitment requirement, and the resumes are classified and evaluated according to the plurality of recruitment positions after the keywords of the plurality of the arranged recruitment positions are extracted, so that all resumes with the scores higher than the preset screening threshold in the plurality of the recruitment positions can be screened from the resume database at one time, the simultaneous recruitment of the plurality of the positions is realized, and the talent recruitment efficiency of enterprises is further improved.
Example three:
fig. 3 shows a schematic structural diagram of a resume screening apparatus provided in an embodiment of the present application, and for convenience of description, only parts related to the embodiment of the present application are shown:
this resume sieving mechanism includes: the data acquisition unit 31, the keyword extraction unit 32, the resume classification evaluation unit 33, and the resume screening unit 34. Wherein:
the data acquisition unit 31 is configured to acquire resume information of an applicant to obtain a first resume.
The resume information of the applicant can be collected according to resume data filled in by the applicant on a website or an electronic resume directly delivered by the applicant on the internet, so that the first resume is obtained and stored in the resume database.
The first resume can include at least one of the name, sex, academic record, graduation colleges, specialties, achievements, community experience, social work experience and the like of the applicant.
In this embodiment, resume information of an applicant is collected according to a collection range set by a user, where the collection range may be a website range corresponding to different websites, or a range corresponding to different pages in the same website.
And the keyword extraction unit 32 is configured to perform keyword extraction on the first resume to obtain a second resume with keywords.
In order to quickly and accurately screen out the required resume, keyword extraction is carried out on each first resume in the resume database to obtain a second resume with keywords, so that subsequent resume screening is facilitated. The number of the keywords is plural, and the keywords may include words describing information such as a academic calendar, a college, a discipline specialty, and an english level. For example, the keywords describing the academic calendar may be elementary school, junior high school, this subject, student, doctor, etc.
Optionally, the keyword extraction unit extracts the keywords of the first resume through a pre-trained keyword extraction model to obtain a second resume with the keywords.
Specifically, a keyword extraction model is trained in advance through a deep learning algorithm and used for extracting keywords of the first resume, and the deep learning algorithm can acquire more accurate information, so that when the keywords of the first resume are extracted through the keyword extraction model obtained through training of the deep learning algorithm, effective information in the first resume can be extracted more accurately and efficiently, and a second resume only with keyword information concerned by enterprises is obtained.
Optionally, the keyword extraction unit includes a keyword extraction model training module, configured to train a model for extracting keywords by using a keyword library as a label and using a preset resume as a sample.
In the model training stage, a keyword library is firstly established, which can comprise a academic keyword library, a university school name keyword library, a subject professional name keyword library, a university English grade keyword library and a score keyword library, a practice company name and position keyword library, a working time and working position keyword library, a college period student working position keyword library, an engaging position and salary keyword library and the like, and a plurality of keyword libraries are selected according to specific needs and packaged into a final keyword library. The plurality of keyword libraries selected here may be a combination of part or all of the above listed keyword libraries, and may include other optional keyword libraries not listed.
And after the keyword library is established, training a model which can be used for extracting the keywords by taking the finally determined keyword library as a label and taking resume information of some applicants acquired from the Internet as samples.
And the resume classification evaluation unit 33 is configured to classify the second resume according to a preset recruitment position, and evaluate a score, where the number of the preset recruitment positions is two or more.
And classifying the second resume with the keywords according to the preset recruitment positions, firstly obtaining the classification probability of the second resume being classified into each preset recruitment position, and finally classifying the second resume into the preset recruitment position with the highest corresponding classification probability. And when the classification probabilities of the second resume classified into the preset recruitment positions are the same and the second resume is the highest, distributing the second resume to the preset recruitment positions at the same time, namely, the second resume corresponds to a plurality of recruitment positions. And after the classification, evaluating and scoring the second resumes in the preset recruitment positions assigned with the second resumes respectively to obtain second resume scores S in the preset recruitment positions. Wherein, the number of the preset recruitment positions is two or more, and the resumes can be classified.
Optionally, the resume evaluation unit classifies the second resume according to a preset recruitment position and evaluates the score through a pre-trained text classification evaluation model.
Optionally, the resume classification evaluation unit includes a classification module and an evaluation score module.
And the classification module is used for classifying the second resume according to the preset recruitment position through a pre-trained text classification evaluation model, and simultaneously returning the classification probability that the second resume is classified into the preset recruitment position.
And the evaluation score module is used for determining the evaluation score of the second resume according to the classification probability.
And the resume screening unit 34 is configured to screen the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold.
Optionally, the resume screening apparatus further includes a setting unit, configured to receive a setting instruction, where the setting instruction includes setting information of the recruitment position and setting information of the screening threshold; and carrying out resume classification screening according to the setting information.
Specifically, when the preset recruitment positions and the screening threshold do not exist or the preset information needs to be changed, a setting instruction is received, the recruitment positions and the corresponding screening threshold are set according to the current recruitment need, wherein the number of the set recruitment positions is multiple, and each recruitment position is correspondingly provided with one screening threshold. And after the setting, storing the setting information as the preset recruitment position and the corresponding preset screening threshold value for subsequent resume classification screening.
Optionally, the resume screening apparatus further includes a sending unit, configured to send the screened first resume.
And sending the resume screened from the resume database by the first resume to the enterprise, and sending the screened resume to the recruitment department through prestored mailbox information of the enterprise recruitment department so as to inform the recruitment personnel of the enterprise in time. Specifically, a time period can be preset, and the first resume corresponding to each screened preset recruitment position is respectively packaged and sent to the enterprise at intervals, so that the enterprise can perform subsequent recruitment work.
In the embodiment of the invention, as the number of the preset recruitment positions is multiple, the resumes are classified and evaluated according to the preset recruitment positions after the keywords of the resumes are extracted, all the resumes with the scores higher than the preset screening threshold value in the plurality of the recruitment positions can be screened from the resume database at one time, the simultaneous recruitment of the multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example four:
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40 executes the computer program 42 to implement the steps in the above-mentioned embodiments of the resume filtering method, such as the steps S101 to S104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the units 31 to 34 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into a data acquisition unit, a keyword extraction unit, a resume classification evaluation unit, and a resume screening unit, and each unit has the following specific functions:
the data acquisition unit is used for acquiring resume information of an applicant to obtain a first resume.
And the keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords.
And the resume classification evaluation unit is used for classifying the second resume according to a preset recruitment position and evaluating a score, wherein the number of the preset recruitment positions is two or more.
And the resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A resume screening method is characterized by comprising the following steps:
acquiring resume information of an applicant to obtain a first resume;
extracting keywords of the first resume to obtain a second resume with the keywords;
classifying the second resume according to a preset recruitment position, wherein the number of the preset recruitment positions is two or more;
and screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value.
2. The resume screening method of claim 1, wherein the extracting the keyword of the first resume to obtain a second resume with the keyword specifically comprises:
and extracting the keywords of the first resume through a pre-trained keyword extraction model to obtain a second resume with the keywords.
3. The resume screening method of claim 2, wherein the pre-trained keyword extraction model specifically comprises:
and training a model for extracting the keywords by taking the keyword library as a label and taking a preset resume as a sample.
4. The resume screening method of claim 1, further comprising, prior to said classifying the second resume by a preset recruitment position and evaluating a score:
receiving a setting instruction, wherein the setting instruction comprises setting information of a recruitment position and setting information of a screening threshold;
and carrying out resume classification screening according to the setting information.
5. The resume screening method of claim 1, wherein the classifying the second resume according to the preset recruitment position and evaluating the score comprises:
and classifying the second resume according to a preset recruitment position and evaluating the score through a pre-trained text classification evaluation model.
6. The resume screening method of claim 5, wherein the classifying the second resume according to the preset recruitment position and evaluating the score through the pre-trained text classification evaluation model specifically comprises:
classifying the second resume according to the preset recruitment position through a pre-trained text classification evaluation model, and simultaneously returning the classification probability of the second resume to the preset recruitment position;
determining an evaluation score of the second resume according to the classification probability.
7. The resume screening method of any one of claims 1 to 6, further comprising, after the screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value:
and sending the screened first resume.
8. A resume screening device, comprising:
the data acquisition unit is used for acquiring resume information of an applicant to obtain a first resume;
the keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords;
the resume classification evaluation unit is used for classifying the second resume according to a preset recruitment position and evaluating scores, wherein the number of the preset recruitment positions is two or more;
and the resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN201811375933.3A CN111198943A (en) | 2018-11-19 | 2018-11-19 | Resume screening method and device and terminal equipment |
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| CN201811375933.3A CN111198943A (en) | 2018-11-19 | 2018-11-19 | Resume screening method and device and terminal equipment |
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