CN115935958A - Resume processing method and device, storage medium and electronic equipment - Google Patents
Resume processing method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a resume processing method and device, a storage medium and electronic equipment. Wherein, the method comprises the following steps: acquiring a target resume and extracting text information of the target resume; determining a subject word of the target resume based on the text information, wherein the subject word is used for representing a text subject to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets the label condition; integrating the subject term and the label term to obtain a target characteristic term; and determining the resume attributes of the target resume according to the target characteristic words, and processing the target resume by using a processing mode of resume attribute matching. The method and the device solve the technical problem that resume processing efficiency is low.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a resume processing method, apparatus, storage medium, and electronic device.
Background
In a resume processing scene, because the filtering of the essence of the resumes needs certain subjective judgment, the problem that the filtering of the resumes is not accurate enough can be caused by only using a simple calculation model to process the resumes, so the resumes are generally processed by using a mode of manually marking the resumes, but the mode is generally low in marking quality and difficult to consider the accuracy and the speed at the same time, and the problem that the processing efficiency of the resumes is low is caused. Therefore, there is a problem that the resume processing efficiency is low.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a resume processing method and device, a storage medium and electronic equipment, and aims to at least solve the technical problem of low resume processing efficiency.
According to an aspect of an embodiment of the present application, there is provided a resume processing method, including: acquiring a target resume and extracting text information of the target resume;
determining a subject term of the target resume based on the text information, wherein the subject term is used for representing a text subject to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions;
integrating the subject term and the label term to obtain a target characteristic term;
and determining the resume attributes of the target resume according to the target characteristic words, and processing the target resume by using the resume attribute matching processing mode.
According to another aspect of the embodiments of the present application, there is also provided a resume processing apparatus, including:
the first acquisition unit is used for acquiring a target resume and extracting text information of the target resume;
a first determining unit, configured to determine a subject term of the target resume based on the text information, where the subject term is used to indicate a text topic to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions;
the first integration unit is used for integrating the subject term and the label term to obtain a target feature term;
and the first processing unit is used for determining the resume attribute of the target resume according to the target characteristic words and processing the target resume by utilizing the resume attribute matching processing mode.
As an optional solution, the first determining unit includes:
the first extraction module is used for extracting a plurality of first phrases from the text information;
a first determining module, configured to determine a confidence corresponding to each of the plurality of first word groups, where the confidence is used to indicate a probability that the first word group is the subject word;
and the second determining module is used for determining a first target phrase with the highest confidence coefficient from the plurality of first phrases and taking the first target phrase as the subject word.
As an optional solution, the first determining module includes:
and the first input submodule is used for inputting the plurality of first phrases into a document theme generation model to obtain the confidence corresponding to each first phrase output by the document theme generation model, wherein the document theme generation model is a Bayesian probability model which is obtained by training phrase samples and is used for identifying the three-level hierarchical structure of the phrases.
As an alternative, the first determining unit includes:
the second extraction module is used for extracting a plurality of second phrases from the text information;
a third determining module, configured to determine a word frequency corresponding to each of the plurality of second word groups, where the word frequency is used to indicate an occurrence frequency of the second word group in the text information;
and the fourth determining module is used for determining a second target phrase with the highest word frequency from the plurality of second phrases and taking the second target phrase as the label word.
As an optional solution, the apparatus further includes:
the first integration module is configured to, before the integrating processing is performed on the subject word and the tag word to obtain a target feature word, include: assigning a first weight to the subject word and a second weight to the tag word, wherein the first weight and the second weight are in a negative correlation relationship, and the second weight and the word frequency of the second target word group are in a positive correlation relationship;
a second integration module, configured to integrate the subject term and the tag term to obtain a target feature term, where the second integration module includes: and integrating the subject term and the label term according to the first weight and the second weight to obtain the target feature term.
As an optional solution, the first obtaining unit includes:
the first acquisition module is used for acquiring a target resume and extracting an initial text of the target resume;
a first processing module, configured to perform preprocessing on the initial text to obtain the text information, where the preprocessing includes at least one of: word segmentation processing and stop word processing.
As an optional solution, the first processing unit includes:
a fifth determining module, configured to determine a processing priority of the target resume according to the target feature word;
the first matching module is used for distributing interview scheduling information to delivery personnel matched with the target resume based on the processing priority, wherein the interview scheduling information comprises at least one of the following information: interview site information, interview time information, interviewer information and interview post information.
According to yet another aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the resume processing method as above.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the resume processing method through the computer program.
In the embodiment of the application, a target resume is obtained, and text information of the target resume is extracted; determining a subject term of the target resume based on the text information, wherein the subject term is used for representing a text subject to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions; integrating the subject term and the label term to obtain a target characteristic term; and determining the resume attribute of the target resume according to the target feature words, and processing the target resume by using the processing mode of matching the resume attribute, so that the aims of obtaining the feature words by integrating the subject words and the label words based on the resume, further determining the resume attribute based on the feature words and processing the resume are fulfilled, and the technical effect of improving the resume processing efficiency is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an application environment of an alternative resume processing method according to an embodiment of the application;
FIG. 2 is a schematic diagram illustrating a flow of an alternative resume processing method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative resume processing method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 6 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 7 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 8 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 9 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 10 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 11 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 12 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 13 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 14 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 15 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 16 is a schematic diagram of another alternative resume processing method according to an embodiment of the present application;
FIG. 17 is a schematic diagram of an alternative resume processing apparatus according to an embodiment of the present application;
fig. 18 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of the embodiments of the present application, a resume processing method is provided, and optionally, as an optional implementation, the resume processing method may be applied to, but is not limited to, an environment as shown in fig. 1. The system may include, but is not limited to, a user device 102 and a server 112, the user device 102 may include, but is not limited to, a display 108, a processor 106 and a memory 104, and the server 112 includes a database 114 and a processing engine 116.
The specific process comprises the following steps:
step S102, the user equipment 102 acquires the text information of the target resume;
steps S104-S106, sending the text information of the target resume to the server 112 through the network 110;
step S108, the server 112 determines subject words and label words of the target resume from the text information of the target resume through the processing engine, obtains target characteristic words through integration processing based on the subject words and the label words, and determines resume attributes of the target resume according to the target characteristic words;
steps S110-S112 are sending the attributes for the resume to the user device 102 via the network 110, the user device 102 processing the resume with the attributes of the resume via the processor 106 and displaying the processing result on the display 108, and storing the attributes of the resume in the memory 104.
In addition to the example shown in fig. 1, the above steps may be performed independently by the client or the server, or may be performed by both the client and the server, for example, the client performs the steps of step S108, so as to relieve the processing pressure of the server. The user equipment 102 includes, but is not limited to, a handheld device (e.g., a mobile phone), a notebook computer, a desktop computer, a vehicle-mounted device, and the like, and the application does not limit the specific implementation manner of the user equipment 102.
Optionally, as an optional implementation manner, as shown in fig. 2, the resume processing method includes:
s202, acquiring a target resume and extracting text information of the target resume;
s204, determining subject words of the target resume based on the text information, wherein the subject words are used for representing text subjects to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets the label condition;
s206, integrating the subject term and the label term to obtain a target feature term;
and S208, determining the resume attribute of the target resume according to the target feature words, and processing the target resume by using a resume attribute matching processing mode.
Optionally, in this embodiment, the resume processing method may be applied to, but not limited to, a resume processing scenario, where the resume processing has a certain subjectivity, a machine learning method and a manual labeling method are generally mainly used in the prior art to process the resume, and in the prior art that uses machine learning, a bayesian formula is generally used to classify and case texts based on keywords, and a most relevant file is returned.
Optionally, in this embodiment, the target resume may be understood as, but not limited to, being composed of personal basic information, self-introduction, work experience, and the like; the text information of the target resume can be understood as text information including characters, pictures and other information in the resume. Is a sentence or a combination of sentences having a certain meaning.
Optionally, in this embodiment, determining the subject term of the target resume based on the text information may include, but is not limited to, extracting text information in the target resume, determining a subject term including a summary attribute based on the text information, extracting a plurality of keywords in an original text of the resume, further screening out a keyword that best matches a core of the resume as the subject term of the target resume based on the plurality of keywords in the original text, and also may include, but is not limited to, summarizing contents of the original resume, summarizing a new keyword, for example, a keyword "hobby exercise" is summarized in text information contents of a small-sized resume, and the keyword "hobby exercise" is summarized as the subject term of the target resume based on the text information.
Optionally, in this embodiment, the determining the tag words of the target resume based on the text information may include, but is not limited to: the method includes the steps that text information with high occurrence frequency in resume text information is used as label words, a plurality of label words can be provided, a frequency threshold value can be preset, phrases reaching the preset frequency threshold value in the resume text information are used as the label words, but the method is not limited to the steps that important label words are preset under the condition that the resume is received according to information of posts, companies and the like, and words which accord with the preset important label words in the resume text information are preferentially processed or matched.
Optionally, in this embodiment, the subject terms may be, but are not limited to, a limited number, and in most cases, only one subject term may be limited in the target resume, and the setting of the tag terms may be a limited number or an unlimited number.
Optionally, in this embodiment, the subject word and the tag word are integrated to obtain the target feature word, and the resume attribute is determined based on the target feature word, which may be, but is not limited to, determining that the attribute of the resume is preferentially processed or temporarily unprocessed based on the integration of the subject word and the tag word, and further processing the resume based on the attribute of the resume.
It should be noted that the processing nature of the resume has certain subjectivity, and if the text information extracted from the resume is directly input into the machine learning model, the output resume mode is relatively solidified, and the diversity and accuracy of resume processing cannot be ensured, but if all resumes are processed by using an artificial labeling method, the processing efficiency is relatively low, and the problem that the accuracy and efficiency of resume processing are difficult to be considered exists.
For further example, optionally as shown in fig. 3, the target resume 302 is obtained, text information in the target resume 302 is extracted, a subject word 304 in the target resume 302 is determined, a tag word 306 of the target resume is determined, the subject word 302 and the tag word 306 are integrated to obtain a target feature word, a resume attribute 308 of the target resume is determined based on the target feature word, and the target resume 302 is processed by using the resume attribute 308.
According to the embodiment provided by the application, the target resume is obtained, and the text information of the target resume is extracted; determining subject words of the target resume based on the text information, wherein the subject words are used for representing text subjects to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions; integrating the subject term and the label term to obtain a target characteristic term; the resume attribute of the target resume is determined according to the target feature words, the target resume is processed by a processing mode of resume attribute matching, and therefore the purposes that the feature words are obtained by integrating the subject words and the label words based on the resume, the resume attribute is further determined based on the feature words, and the resume is processed are achieved, and the technical effect of improving the resume processing efficiency is achieved.
As an alternative, determining the subject term of the target resume based on the text information includes:
extracting a plurality of first phrases from the text information;
determining a confidence corresponding to each first phrase in the plurality of first phrases, wherein the confidence is used for representing the probability that the first phrase is a subject word;
and determining a first target phrase with the highest confidence coefficient from the plurality of first phrases, and taking the first target phrase as a subject word.
Optionally, in this embodiment, it may be, but is not limited to, extracting text information of the target resume, further extracting a plurality of first word groups from the text information, further calculating a probability that a confidence corresponding to each first word group is determined as a subject word of the first word group, and selecting the first word group with the highest confidence as the subject word.
According to the embodiment provided by the application, a plurality of first phrases are extracted from text information; determining a confidence coefficient corresponding to each first phrase in the plurality of first phrases, wherein the confidence coefficient is used for representing the probability that the first phrase is a subject word; the first target phrase with the highest confidence coefficient is determined from the plurality of first phrases, and the first target phrase is used as the subject term, so that the purpose of determining the subject term based on the confidence coefficients of the plurality of phrases is achieved, and the technical effect of improving the accuracy of determining the subject term is achieved.
As an optional scheme, determining a confidence corresponding to each first phrase in the plurality of first phrases includes:
and inputting a plurality of first phrases into a document theme generation model to obtain the confidence corresponding to each first phrase output by the document theme generation model, wherein the document theme generation model is a Bayes probability model which is obtained by training phrase samples and is used for identifying the three-level hierarchical structure of the phrases.
Optionally, in this embodiment, the method may be, but is not limited to, inputting a plurality of phrases into a document theme to generate a machine learning model, and obtaining a corresponding confidence level of a model output, where the document theme model is obtained by performing sample training on the phrases in advance, and is used as a bayesian probability model for identifying a three-level hierarchical structure of the phrases.
According to the embodiment provided by the application, a plurality of first phrases are input into the document theme generation model, and the confidence corresponding to each first phrase output by the document theme generation model is obtained, wherein the document theme generation model is a Bayesian probability model which is obtained by training phrase samples and used for identifying the three-level hierarchical structure of the phrases, so that the purpose of determining the confidence by using the model is achieved, and the technical effect of improving the accuracy is achieved.
As an alternative, the determining of the tag words of the target resume based on the text information includes:
extracting a plurality of second phrases from the text information;
determining a word frequency corresponding to each of the plurality of second phrases, wherein the word frequency is used for indicating the occurrence frequency of the second phrases in the text information;
and determining a second target phrase with the highest word frequency from the plurality of second phrases, and taking the second target phrase as a tag word.
Optionally, in this embodiment, the tag word may be, but is not limited to, determined according to the frequency of occurrence of a certain word group in the resume text information, a second word group is first extracted from the text information, and based on the frequency of occurrence of the second word group in the text information, a target word group with the highest frequency of occurrence is determined as the tag word.
Optionally, in this embodiment, the tag words may be directly screened based on the words in the original resume, or may be summarized new words, which is not limited herein.
According to the embodiment provided by the application, a plurality of second phrases are extracted from the text information;
determining a word frequency corresponding to each second phrase in the plurality of second phrases, wherein the word frequency is used for indicating the occurrence frequency of the second phrases in the text information;
and determining a second target phrase with the highest word frequency from the plurality of second phrases, and using the second target phrase as a tag word, thereby achieving the purpose of determining the tag word, and further achieving the technical effect of improving the accuracy of determining the tag word.
As an optional scheme, before integrating the subject word and the tag word to obtain the target feature word, the method includes: assigning a first weight to the subject word and a second weight to the tag word, wherein the first weight and the second weight are in a negative correlation relationship, and the second weight and the word frequency of the second target word group are in a positive correlation relationship;
integrating the subject word and the tag word to obtain a target feature word, wherein the target feature word comprises the following steps: and integrating the subject term and the label term according to the first weight and the second weight to obtain the target characteristic term.
Optionally, in this embodiment, the integrating process may, but does not absorb, assign a first weight to the subject word based on a specific word frequency, a preset keyword, and the like, assign a second weight to the tag word, where the first weight and the second weight are in a negative correlation relationship, and finally perform the integrating process based on the first weight and the second weight and the subject word and the tag word to obtain the target feature word.
It should be noted that, when a word group has a higher word frequency, a larger weight may be assigned to the tag word, and when a subject of a word group is more definite, or when the word groups in the resume are more uniformly distributed and the word frequencies are more uniform, a first weight is assigned to the subject word, and a second weight is assigned to the tag word.
Through the embodiment provided by the application, before integrating the subject term and the label term to obtain the target feature term, the method comprises the following steps: assigning a first weight to the subject word and a second weight to the tag word, wherein the first weight and the second weight are in a negative correlation relationship, and the second weight and the word frequency of the second target word group are in a positive correlation relationship;
integrating the subject term and the label term to obtain a target feature term, comprising: and integrating the subject term and the label term according to the first weight and the second weight to obtain the target characteristic term, so that the aim of improving the diversity of integration treatment is fulfilled, and the technical effect of determining and improving the accuracy of the target characteristic term is realized.
As an optional scheme, acquiring a target resume, and extracting text information of the target resume includes:
acquiring a target resume, and extracting an initial text of the target resume;
preprocessing the initial text to obtain text information, wherein the preprocessing comprises at least one of the following steps: word segmentation processing and word deactivation processing.
Optionally, in this embodiment, the text may be preprocessed, word segmentation is performed, and word processing is removed, so that subsequent extraction of text information and calculation of word frequency are facilitated.
According to the embodiment provided by the application, the target resume is obtained, and the initial text of the target resume is extracted; preprocessing the initial text to obtain text information, wherein the preprocessing comprises at least one of the following steps: word segmentation processing and stop word processing, thereby realizing the technical effect of improving resume processing efficiency.
As an optional scheme, determining the resume attribute of the target resume according to the target feature word, and processing the target resume by using a processing mode of resume attribute matching, including:
determining the processing priority of the target resume according to the target feature words;
assigning interview scheduling information to delivery people with the matched target resumes based on the processing priority, wherein the interview scheduling information includes at least one of: interview site information, interview time information, interviewer information and interview post information.
Alternatively, in the present embodiment, priority of resume processing may be determined based on the target feature word, but not limited thereto, so that arrangement of subsequent interview information is performed according to the priority.
According to the embodiment provided by the application, the processing priority of the target resume is determined according to the target feature words; assigning interview scheduling information to delivery people with the matched target resumes based on the processing priority, wherein the interview scheduling information includes at least one of: the interview site information, the interview time information, the interview personnel information and the interview post information further achieve the purpose of processing the target resume at this time based on the priority, and therefore the technical effect of flexibly processing and establishing information is achieved.
As an optional scheme, the technical means is applied in a specific resume processing scenario:
optionally, in this embodiment, the resume text needs to be preprocessed first, which is the first step of resume label classification, including word segmentation and stop word removal. English text is a word string separated by spaces on a character set, chinese text is not separated by spaces, and most of the Chinese text is a continuous word string on the character set. Word segmentation is to change continuous word strings in the resume text into word strings. The word segmentation of the Chinese text is particularly important compared to the English text. After the text is participled, the text becomes a collection of individual words. But for the text, there are words that do not contribute to the content expressed by the text, and these words are meaningless for the classification of the text, which are called stop words. Stop words typically include mood words, auxiliary words, adverbs, prepositions, punctuation, and the like. Therefore, after segmenting the resume text, the resume text is de-stop.
The resume text can be saved, in this embodiment, after being preprocessed
The meaning of the text and the meaning of the words cannot be recognized by the computer by directly inputting the document set. One way to solve this problem is to represent the text as a way that can be handled by a computer. The vector space model is a commonly used text representation method. Text is represented herein using a vector space model.
The vector space model rationale can be described as: for any given text D, the text may be represented as D = (t) 1 :w 1 ,t 2 :w 2 ,...,t n :w n ) Wherein t is i Representing selected feature items in news text, each feature item being non-repeating, w i Expressed as the weight of a news text feature item, when a document is expressed as an n-dimensional vector consisting of a plurality of feature items, we call D = (t) 1 :w 1 ,t 2 :w 2 ,...,t n :w n ) Abbreviated as D = (w) 1 ,w 2 ,...,w n ) And is a vector representation of the text.
After being represented by a vector space model, the document set is represented as a matrix, w mn And representing the weight of the nth characteristic word of the mth text as follows:
optionally, in this embodiment, the LDA model may be used for extracting the tag words, and the model is a bayesian model of a three-level hierarchical structure of words, topics and documents. The sample is regarded as fixed, and the previous knowledge of things is continuously corrected through sample information. The generation of a text is as follows: firstly, a certain theme is selected with a certain probability, a certain word is selected from the theme with a certain probability, and the process is repeated until a text is generated. The distribution of the subject and the distribution of the words are not definitely constant and cannot be exactly given. The distribution of topics and words is randomly determined by a priori parameters. The LDA generative model is shown in FIG. 4, except that w m,n As is known, both α and β are generally determined in advance as a rule empirically, Z m,n 、θ m 、
Determined by LDA model. The explanation of the parameters is shown in fig. 5: and alpha and beta areThe Dirichlet prior distribution of the LDA model respectively represents prior parameters of topic distribution on the whole document set and prior parameters of word distribution on all topics; generating a distribution theta of the subjects in the document through alpha; selecting a theme Z according to the theta distribution of the document; generating a characteristic word distribution ^ of the subject Z by beta>
From a theme Z>
A term ω is obtained in the distribution. And repeatedly generating M documents.
Optionally, in this embodiment, for example, as shown in fig. 6, feature words in the resume are selected according to the word frequency, the subject words of the resume text are extracted by LDA modeling, and the tag words selected according to the word frequency and the subject words selected by LDA are combined.
Optionally, in this embodiment, an SSM framework is adopted in the background development technology of the system. The SSM framework is composed of three frameworks, namely Spring, spring MVC and MyBatis, and divides an application component into four layers, namely a view layer, a controller layer, a service layer and a dao layer. And combining the view layer and the controller layer to form the view layer. Each layer has clear labor division and is interacted through an interface, so that the development efficiency is greatly improved. The concrete frame structure is shown in fig. 7, the presentation layer in the SSM frame is composed of JSP, spring MVC and Action classes, the Service layer is realized by Service interface and Service, and the persistence layer is realized by Mybatis and is composed of dao interface and dao. Spring functions to manage creation, destruction, relationships between objects, etc. of objects in a project. Mybatis is used as an implementer of dao to complete the access of the database and store the returned data, so that the data operation in the database can be realized only by calling the data model operation in the service layer, and decoupling and code multiplexing are facilitated. The Service logic layer is realized by Service, a user requests Service through Action class to reach the Service layer, the specific Service realizes data exchange processing in the Service logic, and processes a returned result and returns the result to the controller in Spring MVC. The presentation layer is used for requesting service, the control layer receives the result processed by the service layer and transmits the result to the page in json format, and the page is displayed to the user through data rendering.
Optionally, in the present embodiment, the recruitment system functional architecture is shown in fig. 8, for example, and the recruitment management system is mainly composed of a front platform and a back platform. The foreground application comprises functional modules of social recruitment, campus recruitment, internal talent market, resume management, delivery management and the like; the background comprises function modules of job management, candidate management, an internal talent bank, an external talent bank, role management, user management and the like.
Specifically, for example, as shown in fig. 9, resume management: personal resume information can be maintained in resume management, the type of information on the left side of a page is selected, and corresponding information can be maintained by clicking and editing; for example, as shown in fig. 10, delivery management: the applied positions can check resume processing conditions in delivery management, resumes in the to-be-processed and eliminated states can be withdrawn, and resumes in other states can not be withdrawn for delivery.
For example, as shown in fig. 11, job management: the recruitment reception can be divided into social recruitment, campus recruitment, internal talent market and postdoctor recruitment according to the recruitment types. Target positions can be searched according to the recruitment types, the work places and the like; and (3) job management: the corresponding position information can be added, modified and downloaded in the position management to know the recruitment condition of the position, for example: the number of people dropped, the number of people having read, and the number of people trying out; the job management support manages job information at different stages, including: released, offline, saved, pending and rejected. For example, as shown in fig. 12, job approval: all the job information that need to be examined and approved of this company is shown in the job examination and approval, divide into: the examination and approval are passed; the position examination and approval supports single examination and approval and batch examination and approval; two account numbers are set for each company, HR1 is responsible for job entry, and HR2 is responsible for job audit. For example, as shown in fig. 13, candidate management: the candidate menu shows the relevant information of the candidates of all the recruitment positions of the company; the candidate supports screening and filtering resumes of the candidate, sends a notification to the candidate, and arranges interviewing, enrollment and the like for the candidate; meanwhile, the resume of each candidate is also labeled by the classifier. For example, as shown in fig. 14, the internal talent bank: the internal talent base supports checking resume information delivered by the enterprise headquarter and the candidate of each level of subsidiary company and supports label search of resumes. For example, as shown in fig. 15, an external talent bank: the external talent base supports checking of candidate information outside the enterprise system and supports label search resumes.
Optionally, in this embodiment, as shown in fig. 16, for example, in the overall process of the recruitment system, first, the HR issues a job in the background management system, and a recruitment type may be selected: recruitment types such as campus recruitment, social recruitment, internal talent market and the like; after successful release, the applicant can see the newly added post information in the corresponding recruitment type module in the foreground management system and can deliver the post information; the applicant maintains the resume information in the resume management module at first, and post delivery can be carried out after the maintenance is finished; the post which has been delivered can be checked in the delivery management; after delivery is completed, the HR can see a new resume in a background management system, the resume information is classified by a classifier to carry out label classification, and corresponding label information can be seen at the remarkable position of the resume, so that the HR can conveniently and quickly position whether a candidate is matched with a post or not; all resumes flowing into the background management system finally enter the talent base no matter whether the interview is successful or not, and when enterprises need talents next time, related resumes can be searched and matched in the talent base according to the resume labels.
It is understood that in the specific implementation of the present application, related data such as user information is involved, when the above embodiments of the present application are applied to specific products or technologies, user permission or consent needs to be obtained, and the collection, use and processing of related data need to comply with relevant laws and regulations and standards in relevant countries and regions.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
According to another aspect of the embodiment of the application, a resume processing device for implementing the resume processing method is also provided. As shown in fig. 17, the apparatus includes:
a first obtaining unit 1702, configured to obtain a target resume and extract text information of the target resume;
a first determining unit 1704, configured to determine a subject word of the target resume based on the text information, where the subject word is used to represent a text subject to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets the label condition;
a first integration unit 1706, configured to integrate the subject term and the tag term to obtain a target feature term;
the first processing unit 1708 is configured to determine a resume attribute of the target resume according to the target feature word, and process the target resume by using a processing manner of resume attribute matching.
For a specific embodiment, reference may be made to the example shown in the resume processing apparatus, and details in this example are not described herein again.
As an alternative, the first determining unit includes:
the first extraction module is used for extracting a plurality of first phrases from the text information;
the first determining module is used for determining a confidence coefficient corresponding to each first phrase in the plurality of first phrases, wherein the confidence coefficient is used for representing the probability that the first phrase is a subject word;
and the second determining module is used for determining a first target phrase with the highest confidence coefficient from the plurality of first phrases and taking the first target phrase as a subject word.
For a specific embodiment, reference may be made to the example shown in the resume processing method, which is not described herein again.
As an optional solution, the first determining module includes:
and the first input submodule is used for inputting a plurality of first phrases into the document theme generation model to obtain the confidence corresponding to each first phrase output by the document theme generation model, wherein the document theme generation model is a Bayes probability model which is obtained by training phrase samples and is used for identifying the three-level hierarchical structure of the phrases.
For a specific embodiment, reference may be made to the example shown in the resume processing method, and details in this example are not described herein again.
As an alternative, the first determining unit includes:
the second extraction module is used for extracting a plurality of second phrases from the text information;
the third determining module is used for determining word frequency corresponding to each second phrase in the plurality of second phrases, wherein the word frequency is used for indicating the occurrence frequency of the second phrases in the text information;
and the fourth determining module is used for determining a second target phrase with the highest word frequency from the plurality of second phrases and taking the second target phrase as a tag word.
For a specific embodiment, reference may be made to the example shown in the resume processing method, which is not described herein again.
As an optional scheme, the apparatus further comprises:
the first integration module is used for integrating the subject term and the label term to obtain a target feature term, and comprises: assigning a first weight to the subject word and a second weight to the tag word, wherein the first weight and the second weight are in a negative correlation relationship, and the second weight and the word frequency of the second target word group are in a positive correlation relationship;
the second integration module is used for integrating the subject term and the label term to obtain a target feature term, and comprises: and integrating the subject term and the label term according to the first weight and the second weight to obtain the target characteristic term.
For a specific embodiment, reference may be made to the example shown in the resume processing method, which is not described herein again.
As an optional solution, the first obtaining unit includes:
the first acquisition module is used for acquiring the target resume and extracting the initial text of the target resume;
the first processing module is used for preprocessing the initial text to obtain text information, wherein the preprocessing comprises at least one of the following steps: word segmentation processing and word deactivation processing.
For a specific embodiment, reference may be made to the example shown in the resume processing method, and details in this example are not described herein again.
As an alternative, the first processing unit includes:
the fifth determining module is used for determining the processing priority of the target resume according to the target feature words;
the first matching module is used for distributing interview scheduling information to delivery personnel matched with the target resume based on the processing priority, wherein the interview scheduling information comprises at least one of the following: interview site information, interview time information, interviewer information and interview post information.
For a specific embodiment, reference may be made to the example shown in the resume processing method, and details in this example are not described herein again.
According to yet another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the resume processing method, as shown in fig. 18, the electronic device includes a memory 1802 and a processor 1804, the memory 1802 stores therein a computer program, and the processor 1804 is configured to execute the steps in any one of the method embodiments described above through the computer program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a target resume and extracting text information of the target resume;
s2, determining subject words of the target resume based on the text information, wherein the subject words are used for representing text subjects to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets the label condition;
s3, integrating the subject term and the label term to obtain a target feature term;
and S4, determining the resume attribute of the target resume according to the target feature words, and processing the target resume by using a resume attribute matching processing mode.
Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 18 is only an illustration, and the electronic device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 18 is a diagram illustrating a structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 18, or have a different configuration than shown in FIG. 18.
The memory 1802 may be used to store software programs and modules, such as program instructions/modules corresponding to the resume processing method and apparatus in the embodiments of the present application, and the processor 1804 executes the software programs and modules stored in the memory 1802, so as to execute various functional applications and data processing, that is, to implement the resume processing method. The memory 1802 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1802 can further include memory located remotely from the processor 1804, which can be connected to the terminals over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 1802 may be used for storing, but not limited to, information such as tag words and subject words. As an example, as shown in fig. 18, the memory 1802 may include, but is not limited to, a first obtaining unit 1702, a first determining unit 1704, a first integrating unit 1706, and a first processing unit 1708 in the resume processing apparatus. In addition, other module units in the resume processing apparatus may also be included, but are not limited to, and are not described in detail in this example.
Optionally, the transmitting device 1806 is configured to receive or transmit data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 1806 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmitting device 1806 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In addition, the electronic device further includes: a display 1808 for displaying information such as the subject word and the tag word; and a connection bus 1810 for connecting the respective module components in the above-described electronic apparatus.
In other embodiments, the terminal device or the server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication. The nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, and other electronic devices, may become a node in the blockchain system by joining the Peer-To-Peer network.
According to an aspect of the application, there is provided a computer program product comprising a computer program/instructions containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. When executed by the central processing unit, the computer program performs various functions provided by the embodiments of the present application.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
It should be noted that the computer system of the electronic device is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
The computer system includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) or a program loaded from a storage section into a Random Access Memory (RAM). In the random access memory, various programs and data necessary for the operation of the system are also stored. The central processor, the read-only memory and the random access memory are connected with each other through a bus. An Input/Output interface (i.e., I/O interface) is also connected to the bus.
The following components are connected to the input/output interface: an input section including a keyboard, a mouse, and the like; an output section including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section including a hard disk and the like; and a communication section including a network interface card such as a local area network card, a modem, or the like. The communication section performs communication processing via a network such as the internet. The driver is also connected to the input/output interface as needed. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive as necessary, so that a computer program read out therefrom is mounted into the storage section as necessary.
In particular, according to embodiments of the present application, the processes described in the various method flowcharts may be implemented as computer software programs. For example, embodiments of the present application 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 illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by the central processor, performs various functions defined in the system of the present application.
According to an aspect of the present application, there is provided a computer-readable storage medium from which a processor of a computer device reads computer instructions, the processor executing the computer instructions to cause the computer device to perform the method provided in the above-mentioned various alternative implementations.
Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring a target resume and extracting text information of the target resume;
s2, determining subject words of the target resume based on the text information, wherein the subject words are used for representing text subjects to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions;
s3, integrating the subject term and the label term to obtain a target feature term;
and S4, determining the resume attribute of the target resume according to the target feature words, and processing the target resume by using a resume attribute matching processing mode.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method of the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, 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 coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
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 may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A resume processing method, comprising:
acquiring a target resume and extracting text information of the target resume;
determining a subject term of the target resume based on the text information, wherein the subject term is used for representing a text subject to which the target resume belongs; determining a label word of the target resume based on the text information, wherein the label word is a word group of which the occurrence frequency in the text information meets a label condition;
integrating the subject term and the label term to obtain a target characteristic term;
and determining the resume attributes of the target resume according to the target characteristic words, and processing the target resume by using a processing mode matched with the resume attributes.
2. The method of claim 1, wherein the determining the subject term of the target resume based on the text information comprises:
extracting a plurality of first phrases from the text information;
determining a confidence corresponding to each first phrase in the plurality of first phrases, wherein the confidence is used for representing the probability that the first phrase is the subject word;
and determining a first target phrase with the highest confidence degree from the plurality of first phrases, and taking the first target phrase as the subject word.
3. The method of claim 2, wherein the determining the confidence level corresponding to each of the plurality of first phrases comprises:
and inputting the plurality of first phrases into a document theme generation model to obtain the confidence corresponding to each first phrase output by the document theme generation model, wherein the document theme generation model is a Bayesian probability model which is obtained by training phrase samples and is used for identifying the three-level hierarchical structure of the phrases.
4. The method of claim 1, wherein the determining the tag words of the target resume based on the textual information comprises:
extracting a plurality of second phrases from the text information;
determining a word frequency corresponding to each second phrase in the plurality of second phrases, wherein the word frequency is used for indicating the occurrence frequency of the second phrases in the text information;
and determining a second target phrase with the highest word frequency from the plurality of second phrases, and taking the second target phrase as the label word.
5. The method of claim 4,
before the integrating processing is performed on the subject word and the tag word to obtain the target feature word, the method includes: allocating a first weight to the subject word and a second weight to the tag word, wherein the first weight and the second weight are in a negative correlation relationship, and the second weight and the word frequency of the second target word group are in a positive correlation relationship;
the integrating the subject term and the label term to obtain the target feature term includes: and integrating the subject term and the label term according to the first weight and the second weight to obtain the target feature term.
6. The method according to any one of claims 1 to 5, wherein the obtaining a target resume and extracting text information of the target resume comprises:
acquiring a target resume, and extracting an initial text of the target resume;
preprocessing the initial text to obtain the text information, wherein the preprocessing comprises at least one of the following steps: word segmentation processing and stop word processing.
7. The method according to any one of claims 1 to 5, wherein the determining resume attributes of the target resume according to the target feature words and processing the target resume by using a processing mode of matching the resume attributes comprises:
determining the processing priority of the target resume according to the target feature words;
assigning interview scheduling information to the delivery people matched to the target resume based on the processing priority, wherein the interview scheduling information includes at least one of: interview site information, interview time information, interviewer information and interview post information.
8. A resume processing apparatus, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target resume and extracting text information of the target resume;
a first determining unit, configured to determine a subject term of the target resume based on the text information, where the subject term is used to indicate a text subject to which the target resume belongs; determining label words of the target resume based on the text information, wherein the label words are phrases of which the occurrence frequency in the text information meets label conditions;
the first integration unit is used for integrating the subject term and the label term to obtain a target feature term;
and the first processing unit is used for determining the resume attribute of the target resume according to the target characteristic words and processing the target resume by using a processing mode matched with the resume attribute.
9. A computer-readable storage medium, characterized in that it comprises a stored program, wherein the program is executable by a terminal device or a computer to perform the method of any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
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| CN117094691A (en) * | 2023-10-16 | 2023-11-21 | 四川省瑞人网络科技有限公司 | Human resource management method based on big data platform |
| CN117094691B (en) * | 2023-10-16 | 2024-02-02 | 四川省瑞人网络科技有限公司 | Human resource management method based on big data platform |
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