CN105868408A - Machine learning based recruitment information analyzing system and method thereof - Google Patents
Machine learning based recruitment information analyzing system and method thereof Download PDFInfo
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- CN105868408A CN105868408A CN201610251966.1A CN201610251966A CN105868408A CN 105868408 A CN105868408 A CN 105868408A CN 201610251966 A CN201610251966 A CN 201610251966A CN 105868408 A CN105868408 A CN 105868408A
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Abstract
The invention relates to a machine learning based recruitment information analyzing system and a method thereof. The analyzing system comprises a data acquisition model and a machine learning model, wherein the data acquisition model is used for crawling and analyzing irregular recruitment webpage information of internet recruiting websites and generating training data; the machine learning model comprises a single information source model and a comprehensive model for processing various information sources, the single information source model is obtained through training of single information source module classification data in the training data, the comprehensive model is obtained through training of comprehensive information detailed classification data in the training data, the internet recruiting websites, the data acquisition model and the machine learning model are connected, and the adopted machine learning model is a conditional random field model. The training data is obtained after recruitment webpage information is analyzed, and then is classified in detail, specific statement blocks and phrases in the recruitment information are classified and annotated by the conditional random field model in the machine learning model, data which cannot be matched with a regular expression are processed, and the problem about analysis of the recruitment information of a complicated or new position is effectively solved.
Description
Technical field
The present invention relates to recruitment information resolution system, more specifically refer to that recruitment information based on machine learning resolves system
System and method thereof.
Background technology
Existing recruitment information resolves and extracts enterprises recruitment post with analysis system only with the artificial rule formulated
Every demand and responsibility, and the information resolved effectively can not be analyzed.
But along with the segmentation of social development with industry function, recruitment biographic information complexity is greatly promoted, and writes lattice
Formula difference is relatively big, causes parsing difficulty to primitive solution analysis system.The rule artificially formulated by regular expression can not
Meet the accuracy demand that current recruitment information resolves, with the demand analyzing its recruitment target further.
Summary of the invention
It is an object of the invention to overcome the defect of prior art, it is provided that recruitment information resolution system based on machine learning
And method.
For achieving the above object, the present invention by the following technical solutions: recruitment information resolution system based on machine learning,
Including data collecting model and machine learning model；Described data collecting model crawls the irregular trick of internet recruitment website
Info web is engaged to carry out resolving and generating training data；Described machine learning model includes single information source model and processes all kinds of
The collective model of information source, described single information source model is to be trained by the single information source module classification data in described training data
Gained, described collective model is to be trained gained, described internet by the integrated information exhaustive division data in described training data
Recruitment website, data collecting model are connected with described machine learning model respectively, and the described machine learning model used is defeated
Export the to be fetched program having information after entering irregular text message, this program be according to actual conditions use condition with
Airport model.
Its further technical scheme is: described training data includes module classification data and exhaustive division data, described
Single information source model is trained gained by described module classification data, and described collective model is trained by described exhaustive division data
Arrive.
Its further technical scheme is: described module classification data include job duty, job requirements, emolument welfare, public affairs
Department is introduced and contact method.
Its further technical scheme is: described exhaustive division data are the subclassifications of described module classification data.
Its further technical scheme is: described collective model comprises a module classification model and an exhaustive division mould
Type；Described module classification model by described module classification data training obtain, and described module classification model with described detailed point
Class machine learning model connects.
Present invention also offers the building method of recruitment information resolution system based on machine learning, its concrete steps are such as
Under:
Step 1. passes through internet recruitment website, and preassigned website is scanned for by web crawlers, captures therein
Recruitment website page information, carries out regular expression search to the text message of webpage, by the information that regular expression search is relevant,
And by existing labeling storage；
If step 2. webpage cannot search relevant information by regular expression, or searched by regular expression
Rope information out is comprehensive not, is resolved by same source high-quality regularization data separate regular expression, generates for machine
The training data of device learning model, utilizes existing data label, directly mates mark in mass data, generates big
The training data comprising irregular expression pattern of amount；
Step 3. model training is divided into two steps, first step training module classification annotation model, second step training exhaustive division
Marking model；The model output of the first step will be as the mode input of second step；
Step 4. is first unified by all data, carries out combined training, obtains one and can process combining of various types of information sources
Matched moulds type；
Step 5. is for different aforementioned sources, and only with information source data training submodel, (each information source has a mould in utilization
Block sort model and a train classification models), it is used for processing the data that information source is clear and definite, accelerates to resolve the degree of accuracy；
Step 6. utilizes the data that existing regular expression treatment classification is good, it is impossible to the data of coupling extract into one
Step strengthens regular expression and disaggregated model, stroke closed-loop system, strengthens system spreadability and accuracy.
Present invention also offers the operation method of a kind of recruitment information resolution system based on machine learning, its step is such as
Under:
Step 1. inputs recruitment website page address or directly inputs recruitment information；
Step 2. obtains analyzing web page content automatically, according to web page tag and content, utilizes machine learning model and expert
System is by recruitment information rough segmentation block；
Rough segmentation block message is carried out exhaustive division by step 3..
Its further technical scheme is: described step 1, and web page contents includes whether to comprise info web and plain text
Information, whether comprise info web according to webpage or plain text information carry out label after carrying out rough segmentation block.
Its further technical scheme is: described step 2, in the operational process performing whole system, first determines whether net
Whether page content comprises info web, if it is, carry out single information source regular expressions coupling, in order to obtain module classification number
According to；If web page contents is plain text information, then after being processed by machine learning collective model and regular expressions coupling
To module and exhaustive division preliminary data.
Its further technical scheme is: described step 3, and described module classification data are through machine learning single information source mould
After type and regular expressions matching treatment, obtain exhaustive division data；Module and exhaustive division preliminary data through described comprehensively
Described exhaustive division data are obtained after model treatment.
The present invention compared with prior art provides the benefit that: the recruitment information based on machine learning of the present invention resolves system
System, is connected with machine learning model by internet recruitment website, recruitment website page information is obtained training data after analysis,
By training data exhaustive division, the conditional random field models in machine learning model is used to come the concrete language in recruitment information
Sentence block carries out classification annotation with phrase, processes regular expression and processes the data that can not mate, and effectively processes complicated or novel
The recruitment information of position resolves and problem analysis.
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings.
Accompanying drawing explanation
Fig. 1 builds flow chart for specific embodiment of the invention offer recruitment information resolution system based on machine learning；
Fig. 2 provides the operational flow diagram of recruitment information resolution system based on machine learning for the specific embodiment of the invention.
Reference
10 internet recruitment website 101 web crawlers
102 recruitment website page information 11 regular expressions
12 training data 121 single information source module classification data
122 single information source module 123 integrated information exhaustive division data
124 collective model 13 machine learning collective models
131 module classification data 132 exhaustive division data
Detailed description of the invention
In order to more fully understand the technology contents of the present invention, below in conjunction with specific embodiment, technical scheme is entered
One step introduction and explanation, but it is not limited to this.
With reference to the schematic flow sheet shown in Fig. 1-Fig. 2, understand a kind of recruitment information solution based on machine learning of the present invention
Analysis system, can be used in recruitment information resolving, can effectively process the recruitment information solution of complicated or novel position
Analysis and problem analysis.
Recruitment information resolution system based on machine learning, including data collecting model and machine learning model；Described
Data collecting model crawls the irregular recruitment website page information 102 of internet recruitment website 10 to carry out resolving and generate training number
According to 12；Machine learning model includes single information source model and collective model 124, and wherein, single information source model is by training number
Obtaining according to single information source module classification data 121 training in 12, collective model 124 is by the integrated information in training data 12
Exhaustive division data 123 are constituted, and, collective model 124 can process various types of information sources；Internet recruitment website 10 and machine
Learning model connects, and single information source model and collective model are all machine learning model, and internet recruitment website is data
Source.
Above-mentioned machine learning module is a program, provides the program of output after given input, and wherein input is those
Irregular text message, is output as the useful information extracted；This program is mainly the most total according to data actual conditions
The process of knot, specifically have employed Stochastic Conditions field model, here it is a kind of concrete machine learning model.First, network is utilized
Reptile 101 crawls in internet recruitment website 10 and scans for preassigned website, captures recruitment webpage therein letter
Breath 102, carries out regular expression 11 to the text message of webpage and searches for, by the information that regular expression 11 search is relevant, and
Alignment is classified, and one is module classification, and another is exhaustive division (i.e. classifying content).The data grabbed (are i.e. recruited
Information) it is also divided into single information source model and collective model, if webpage cannot search relevant information by regular expression,
Or the information searching for out by regular expression 11 is comprehensive not, now can be by by machine learning model, by info web
102 (comprising the information such as html label) carried out classification analysis, then extracted and generate new regular expression 11；Wherein, if net
Relevant information cannot be searched by regular expression in Ye, or the information searching for out by regular expression 11 is the most complete
Face, the information in webpage generates the training data 12 of machine learning model through resolving, and this training data 12 is again by single information source
Model and the corresponding classification of collective model 124, training data 12 is formed single information source module classification by single information source category of model
Data 121, training data 12 is classified by collective model 124 and is formed integrated information exhaustive division data 123.Wherein, training machine
Study module is a program, provides output after given input, wherein, inputs as irregular text message, is output as extracting
Useful information out, regular expressions 11 is series of rules, it is also possible to be a program, mainly think formulate, and
Machine learning model is automatically to sum up according to data actual conditions to form, and this sums up process automatically mainly by condition random
Produce a model, go out useful text message with this model discrimination.Condition random field can be used in actually used such
General program models, it is also possible to be further directed to the term in recruitment field, is configured the parameter of condition random field, such as,
The popular keyword relevant to recruitment field is searched out, to realize the information of emerging occupation is searched from topical news database
Rope, and its regular expression is dynamically updated.
It addition, above-mentioned regular expression is series of rules (it can be appreciated that a program), it is artificial formulation
Series of rules.
Above-mentioned recruitment information resolution system based on machine learning, by internet recruitment website 10 and machine learning mould
Type connects, and recruitment website page information 102 is obtained training data 12 after analysis, by training data 12 exhaustive division, uses
Conditional random field models in machine learning model carries out classification annotation to the concrete statement block in recruitment information and phrase, place
Reason regular expression 11 processes the data that can not mate, and the recruitment information effectively processing complicated or novel position resolves and analyzes
Problem.More further, above-mentioned training data 12 includes module classification data and exhaustive division data 132, above-mentioned list letter
Breath source model is made up of module classification data, and collective model 124 is made up of exhaustive division data 132, such that number can will be trained
According to the information exhaustive division of 12, in case resolving used with analysis.
It addition, module classification data include job duty, job requirements, emolument welfare, company introduction, the mould such as contact method
Block.
Exhaustive division data 132 are the subclassifications of module classification data, and specific category can be formulated according to information source, bag
Include but be not limited to age requirement, educational requirement, the length of service, working experience, required technical ability etc..
Above-mentioned collective model 124 comprises a module classification model and a train classification models, module classification model
Obtained by the training of described exhaustive division data 132, and train classification models is connected with exhaustive division machine learning model, so,
Both exhaustive division data 132 can be classified, it is also possible to the number that can not will mate in these exhaustive division data 132 further
It is trained classification according to extracting further, improves system spreadability and accuracy.
Additionally provide the concrete steps of building of recruitment information resolution system based on machine learning in the present embodiment:
Step 1. passes through internet recruitment website 10, utilizes web crawlers 101 to crawl recruitment website page information 102, in advance
The website specified scans for, and captures recruitment website therein page information 102, and the text message of webpage is carried out regular expression 11
Search, by the information that regular expression 11 search is relevant, and by existing labeling storage；
If step 2. webpage cannot search relevant information by regular expression, or by regular expression 11
Search information out is comprehensive not, is resolved by same source high-quality regularization data separate regular expression 11, generates and uses
Training data 12 in machine learning model.Utilize existing data label, mass data directly mated mark,
Generate the substantial amounts of training data 12 comprising irregular expression pattern.
Step 3. model training is divided into two steps, first step training module classification annotation model, second step training exhaustive division
Marking model.The model output of the first step will be as the mode input of second step.
Step 4. is first unified by all data, carries out combined training, obtains one and can process combining of various types of information sources
Matched moulds type 124.
Step 5. is for different aforementioned sources, and only with information source data training submodel, (each information source has a mould in utilization
Block sort model and a train classification models), it is used for processing the data that information source is clear and definite, accelerates to resolve the degree of accuracy.
Step 6. utilizes the data that existing regular expression 11 treatment classification is good, it is impossible to the data of coupling extract into
One step strengthens regular expression 11 and disaggregated model, stroke closed-loop system, strengthens system spreadability and accuracy.
Due to current recruitment website enormous amount, provided Information Granularity disunity, and recruitment information publisher is to duty
Position functional positioning disunity, these problems cause the significantly lifting of recruitment information complexity.And it is existing based on canonical table
The regularization system reaching formula 11 can not process the input of irregular information, and is difficult to when complexity is the highest improve further, this
Cause system can not be effectively matched the information input not accounted for.The present invention based on original regular expression 11 rule is being
System, for which are added machine learning intelligent object, the recruitment information that can effectively process complicated or novel position resolves and divides
Analysis problem.
Due to original regular expression 11 system can only be good designed by accurate match rule author language performance pattern,
Can not Fuzzy Processing matter of semantics.And machine learning system comprehensive can judge that whether a segment information is with generalizing match requirement
Required matching field.
System based on independent regular expression 11 can not effectively find the defect of designed regular expression 11.And institute
Add machine learning system can help designer find improve regular expression 11 have which potential problems not process, can
To help to design more preferable regular expression 11.
It addition, the present embodiment additionally provides the operational process of recruitment information resolution system based on machine learning:
Step 1. inputs recruitment website page address or directly inputs recruitment information.
Step 2. obtains analyzing web page content automatically, according to web page tag and content, utilizes machine learning model and expert
System is by recruitment information rough segmentation block.
Rough segmentation block message is carried out exhaustive division by step 3..
More further, in step 1, above-mentioned web page contents includes whether to comprise info web and plain text letter
Breath, whether comprise info web according to webpage or plain text information carry out label after carrying out rough segmentation block.
It addition, in step 2, in the operational process performing whole system, first determine whether whether web page contents comprises net
Page information (comprises the information such as html label), if it is, carry out single information source regular expressions coupling, in order to obtain module classification
Data；If web page contents is plain text information, then processed by machine learning collective model 13 and regular expressions coupling
After obtain module and exhaustive division preliminary data.
More further, in step 3, above-mentioned module classification data are through machine learning single information source model and canonical
After expression matching processes, obtain exhaustive division data 132.
It addition, in step 3, above-mentioned module obtains after collective model 124 processes with exhaustive division preliminary data
Exhaustive division data 132.Such that the recruitment information parsing that can realize effectively processing complicated or novel position is asked with analysis
Topic.
The above-mentioned technology contents only further illustrating the present invention with embodiment, in order to reader is easier to understand, but not
Representing embodiments of the present invention and be only limitted to this, any technology done according to the present invention extends or recreation, all by the present invention's
Protection.Protection scope of the present invention is as the criterion with claims.
Claims (10)
1. recruitment information resolution system based on machine learning, it is characterised in that include data collecting model and machine learning mould
Type；Described data collecting model crawls the irregular recruitment website page information of internet recruitment website to carry out resolving and generating training
Data；Described machine learning model includes single information source model and processes the collective model of various types of information sources, described single information
Source model is to be trained gained by the single information source module classification data in described training data, and described collective model is by described instruction
Practice the integrated information exhaustive division data training gained in data, described internet recruitment website, data collecting model respectively with
Described machine learning model connects, and the described machine learning model used is wanted by exporting after the irregular text message of input
The program having information extracted, this program is to use conditional random field models according to actual conditions.
Recruitment information resolution system based on machine learning the most according to claim 1, it is characterised in that described training number
According to including module classification data and exhaustive division data, described single information source model is trained institute by described module classification data
, described collective model is obtained by the training of described exhaustive division data.
Recruitment information resolution system based on machine learning the most according to claim 2, it is characterised in that described module is divided
Class data include job duty, job requirements, emolument welfare, company introduction and contact method.
Recruitment information resolution system based on machine learning the most according to claim 3, it is characterised in that described detailed point
Class data are the subclassifications of described module classification data.
Recruitment information resolution system based on machine learning the most according to claim 4, it is characterised in that described comprehensive mould
Type comprises a module classification model and an exhaustive division model；Described module classification model is instructed by described module classification data
Get, and described module classification model is connected with described exhaustive division machine learning model.
6. the building method of recruitment information resolution system based on machine learning, it is characterised in that build and specifically comprise the following steps that
Step 1. passes through internet recruitment website, and preassigned website is scanned for by web crawlers, captures recruitment therein
Info web, carries out regular expression search to the text message of webpage, by the information that regular expression search is relevant, and presses
Existing labeling storage；
If step 2. webpage cannot search relevant information by regular expression, or searched out by regular expression
The information come is comprehensive not, is resolved by same source high-quality regularization data separate regular expression, generates for engineering
Practise the training data of model, utilize existing data label, mass data is directly mated mark, generates substantial amounts of
Comprise the training data of irregular expression pattern；
Step 3. model training is divided into two steps, first step training module classification annotation model, second step training exhaustive division mark
Model；The model output of the first step will be as the mode input of second step；
Step 4. is first unified by all data, carries out combined training, obtains a comprehensive mould that can process various types of information sources
Type；
Step 5. is for different aforementioned sources, and only with information source data training submodel, (each information source has a module to divide in utilization
Class model and a train classification models), it is used for processing the data that information source is clear and definite, accelerates to resolve the degree of accuracy；
Step 6. utilizes the data that existing regular expression treatment classification is good, it is impossible to the data of coupling extract increasing further
Strong regular expression and disaggregated model, stroke closed-loop system, strengthen system spreadability and accuracy.
7. the operation method of a recruitment information resolution system based on machine learning, it is characterised in that operating procedure is as follows:
Step 1. inputs recruitment website page address or directly inputs recruitment information；
Step 2. obtains analyzing web page content automatically, according to web page tag and content, utilizes machine learning model and expert system
By recruitment information rough segmentation block；
Rough segmentation block message is carried out exhaustive division by step 3..
Operation method the most according to claim 7, it is characterised in that described step 1, web page contents includes whether to comprise
Info web and plain text information, whether comprise info web according to webpage or plain text information carry out label after carrying out thick
Piecemeal.
Operation method the most according to claim 8, it is characterised in that described step 2, in the operation performing whole system
In flow process, first determine whether whether web page contents comprises info web, if it is, carry out single information source regular expressions coupling, with
Just module classification data are obtained；If web page contents is plain text information, then by machine learning collective model and regular expressions
Coupling obtains module and exhaustive division preliminary data after processing.
Operation method the most according to claim 9, it is characterised in that described step 3, module classification data are through machine
After device study single information source model and regular expressions matching treatment, obtain exhaustive division data；Module is preliminary with exhaustive division
Data obtain described exhaustive division data after described collective model processes.
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Cited By (7)
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| CN105868968A (en) * | 2016-04-21 | 2016-08-17 | 广州爱拼信息科技有限公司 | Recruitment information analysis system and method based on machine learning |
| CN107977399A (en) * | 2017-10-09 | 2018-05-01 | 北京知道未来信息技术有限公司 | A kind of English email signature extracting method and system based on machine learning |
| CN108509561A (en) * | 2018-03-23 | 2018-09-07 | 山东合天智汇信息技术有限公司 | Post recruitment data screening method, system and storage medium based on machine learning |
| CN110363488A (en) * | 2019-05-22 | 2019-10-22 | 郑州铁路职业技术学院 | A kind of human resources configuration expert system based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105868968A (en) * | 2016-04-21 | 2016-08-17 | 广州爱拼信息科技有限公司 | Recruitment information analysis system and method based on machine learning |
| CN107977399A (en) * | 2017-10-09 | 2018-05-01 | 北京知道未来信息技术有限公司 | A kind of English email signature extracting method and system based on machine learning |
| CN107977399B (en) * | 2017-10-09 | 2021-11-30 | 北京知道未来信息技术有限公司 | English mail signature extraction method and system based on machine learning |
| CN108509561A (en) * | 2018-03-23 | 2018-09-07 | 山东合天智汇信息技术有限公司 | Post recruitment data screening method, system and storage medium based on machine learning |
| CN108509561B (en) * | 2018-03-23 | 2020-06-26 | 山东合天智汇信息技术有限公司 | Post recruitment data screening method and system based on machine learning and storage medium |
| CN111104798A (en) * | 2018-10-27 | 2020-05-05 | 北京智慧正安科技有限公司 | Analysis method, system and computer readable storage medium for criminal plot in legal document |
| CN111104798B (en) * | 2018-10-27 | 2023-04-21 | 北京智慧正安科技有限公司 | Resolution method, system and computer readable storage medium for sentencing episodes in legal documents |
| CN110363488A (en) * | 2019-05-22 | 2019-10-22 | 郑州铁路职业技术学院 | A kind of human resources configuration expert system based on big data |
| CN112507186A (en) * | 2020-11-27 | 2021-03-16 | 北京数立得科技有限公司 | Webpage element classification method |
| CN112507186B (en) * | 2020-11-27 | 2024-06-14 | 北京数立得科技有限公司 | Webpage element classification method |
| CN113569131A (en) * | 2021-05-14 | 2021-10-29 | 南京奥派信息产业股份公司 | Recruitment corpus labeling method, device, storage medium and equipment |
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