CN110457696A - A kind of talent towards file data and policy intelligent Matching system and method - Google Patents
A kind of talent towards file data and policy intelligent Matching system and method Download PDFInfo
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Abstract
The present invention discloses a kind of talent towards file data and policy intelligent Matching system and method, is editable electronic document by the personal file scanning recognition of papery and forms personnel record information library；Conditional random field models based on event extraction extract the valuable information in personnel record information library and structured storage forms talents information library；Acquisition obtains current talents selection and constructs talents selection database；Policy semantic primitive abstracting method based on artificial rule is extracted to obtain quantifiable indicator information to the talents selection key message of talents selection database；By the matching of the quantifiable indicator information in talents selection database and the field in talents information library and export the talent in all talent's information banks and policy matching result；The policy categories high from matching result screening comprehensive matching degree score value reject the talent to have declared and push policy to the talent for registration and show project information.The present invention is convenient for precisely pushing to not declaring the talent, and attract talent the project of declaring, and provides power-assisted for government's talent introduction.
Description
Technical field
The present invention relates to data processing technique more particularly to a kind of talent towards file data and policy intelligent Matching systems
System and method.
Background technique
The talent is the most crucial element of a regional development, and the outstanding talent is the basis for realizing region development objective, is
The power of regional development.One region first has to pay attention to gathering innovation talent, to try to explore Talent Construction to develop
The new measure, new route and new method, continually strengthen Talent Construction, provide strong human resources for regional development and guarantee.
Since the type of various policies is different, issuing time is different different with affiliated administrative department, these policies is caused to need
It expends considerable time and effort to search for policy source, verifying policy timeliness, assess and declare feasibility etc., this is unfavorable for
The talent fully understands policy information in time, is also not easy to it and filters out from magnanimity policy meet its own policy declared.Institute
With how the policy data of magnanimity is precisely matched to the talent becomes important research direction.
Talents market, office, people society, archives form natural talent bank there are mass talent archive information, but wherein deposit
Archives of paper quality how digitization, talents selection how Auto-matching the problem of.The present invention is based on above-mentioned backgrounds, propose one kind
The talent and policy intelligent Matching system and method towards file data.
The talent and policy matching system are based primarily upon the information progress policy matching that user oneself fills in upload at present, such as specially
Benefit number 201811287900.3 discloses " a kind of policy intelligent Matching system and method ", and the patent system is according to class of subscriber
The basic information for obtaining talent user is filled in selection, user information, carries out successive policy matching.The main problem of the program is base
Policy matching is carried out in the spontaneous upload information of user, cannot achieve extensive automatically talent's matching feature.
The patent No. 201710934706.9 discloses a kind of " matching recommendation side based on city specific crowd with the policy that is associated with
Method and system ", the patent leave family, urban floating population for city, and the old,weak,sick and disabled three classes crowd collects essential information, lead to
It crosses data mining and obtains crowd demand's label, then policy administrative plan of going forward side by side is decomposed based on crowd's label and is recommended.It is with crowd demand
It instructs to push policy to crowd not directed to this special documentation in archives, and guard station office is needed manually to acquire phase
Pass crowd's information, is inevitably omitted, and promotion are not comprehensive enough.
Summary of the invention
The purpose of the present invention is to provide a kind of talent towards file data and policy intelligent Matching system and method, faces
To local archives data, it is automatic to propose that condition random field (CRF) model based on event extraction carries out information to personal file
Extract, while automatic collection talents selection, using the policy semantic primitive abstracting method based on artificial rule parse policy and
Intelligent Matching carries out policy push to talents information, to the talent, realizes the talent and the policy intelligent Matching system of scale automation
System.
The technical solution adopted by the present invention is that:
A kind of talent towards file data and policy intelligent Matching method comprising following steps；
Step 1, obtaining local talent's papery personal file and scanning is image, and will be scanned using image recognition technology
Image be converted into editable electronic document, form personnel record information library；
Step 2, valuable letter is extracted using the conditional random field models based on event extraction based on personnel record information library
It ceases and structured storage forms talents information library；
Step 3, acquisition obtains current talents selection and constructs talents selection database；
Step 4, the policy semantic primitive abstracting method based on artificial rule closes the talents selection of talents selection database
Key information is extracted to obtain quantifiable indicator information；
Step 5, the quantifiable indicator information in talents selection database is matched with the field in talents information library
The talent in all talent's information banks and policy matching result are obtained,
Step 6, from the high policy categories of matching result screening comprehensive matching degree score value reject the talent to have declared to
The talent for registration pushes policy and shows project information.To the policy categories that do not register to the talent in the form of mail, short message into
Administrative plan push shows the declarable project name of the talent, project application economic welfare and the notice link of newest policy.
Further, the specific steps of step 1 are as follows:
Step 1.1, mode typing computer papery personal file scanned, saves as image；
Step 1.2, several subgraphs are divided the image into using OCR technique, includes an individual word in each subgraph
It is female；
Step 1.3, by subgraph from image format conversion at binary format, and by binary data transmission to BP nerve
Network；
Step 1.4, BP neural network finds out the association between character image data and numerical value by training process, will be swept
The image retouched is converted into editable electronic document, and recognition result enters system database.
Further, step 2 carries out the specific steps of event extraction with conditional random field models to personnel record information
Are as follows:
Step 2.1, personnel record information archives text carry out word segmentation processing by document representation at tf/idf weight to
Amount,
Step 2.2, using based on document frequency method carry out feature extraction, filter out for correctly classify contribute it is low
Word；
Step 2.3, archives text training set is set, completes the conversion of archives text training set to characteristic set, and pass through
Artificial mark label learns tagsort；
Step 2.4, archives text test set is obtained into same type characteristic set, is then differentiated by sorter model
To corresponding tag along sort, carries out structured storage and establish talents information library.
The archives text of step 2.1 generally includes personal essential information, education background, work experience and paper Patent Publication
Situation.
Further, the talents selection acquisition modes of step 3 include lead-in mode and extracting mode, and lead-in mode refers to government
Policy information editor is actively stored in talents selection database by mechanism or the third-party institution；Extracting mode refer to using robot,
Web crawlers, Web Spider cyber stalker associated with talent policies all in selected target government website are believed
It ceases progress automatic collection and is downloaded to local server and arrange deposit talents selection database again.
Further, step 4 specifically includes the building of policy dictionary, policy Text Pretreatment and policy information and extracts three steps
Suddenly, specifically:
Policy dictionary building: by the class condition of policy type, policy title, applicable elements and keyword to talents selection
The vocabulary of database carries out including to form talents selection dictionary, and the text feature and description to local talents selection corpus are accustomed to
It is analyzed, extracts the trigger word to the word for playing identification, mark effect of semantic primitive as triggering extraction task.Such as
Declaring condition description generally will appear the vocabulary such as " satisfaction ", " meeting ", " necessary ".
Policy Text Pretreatment: it is segmented using Chinese Academy of Sciences ICTCLAS participle tool and marks part of speech；
Policy information extracts: formulating decimation rule based on triggering vocabulary, and describes decimation rule using regular expression and build
Vertical rule base finally carries out the extraction of the semantic primitive of local talents selection and is stored in talents selection database.
Further, being carried out in step 5 using the matching process based on semantic matches rule will be in talents selection database
Quantifiable indicator information matched with the field in talents information library, specifically includes the following steps:
Step 5.1, matching rule is from the easier to the more advanced sorted, is matched since the index being easiest to, returning to every can quantify
Targets match rules results；
Step 5.2, each matching rule assigns different weights and already present index weights are greater than the index not set up,
And then the matching degree for obtaining the talent and policy categories is calculated, matching degree includes 5 grades: very matching (1 point), matching (0.8
Point), comparison match (0.6 point), general matching (0.4 point), mismatch (0.2 point).If talents information substantially conforms to the project institute
There is matching rule, comprehensive matching similarity then shows that the talent can declare the project close to 1 point.
Step 5.3, all policy categories are retrieved and return to all results and by formation policy after the sequence of comprehensive matching degree
With list.
Further, the policy matching result in step 5 includes project name, type of subject, project application condition, declares
Economic welfare and the notice link of newest policy.
Further, the invention also discloses a kind of talent towards file data and policy intelligent Matching system, packets
It includes with lower module:
Personal file identification module: for being editable electronic document by the personal file scanning recognition of papery and being formed
Personnel record information library；
Personal file property extracting module: the conditional random field models based on event extraction extract personnel record information library
Valuable information and structured storage formation talents information library；
Policy automatic collection module: current talents selection building talents selection database is obtained for acquiring；
Policy segments parsing module: the policy semantic primitive abstracting method based on artificial rule is to talents selection database
Talents selection key message is extracted to obtain quantifiable indicator information；
The talent and policy matching module: in the quantifiable indicator information and talents information library in talents selection database
Field matching and export the talent in all talent's information banks and policy matching result；
Policy pushing module: for rejecting from the high policy categories of matching result screening comprehensive matching degree score value with Shen
The talent of report pushes policy to the talent for registration and shows project information.
The invention adopts the above technical scheme, establishes policy automatic patching system based on local talent's resources bank.Pass through base
Papery talent archive information is identified in the OCR technique of BP neural network, it is automatic to match relevant policies for the local talent and go forward side by side administration
Plan casting push solves the problems, such as that talents selection matching takes time and effort.The present invention passes through the condition random field based on event extraction
(CRF) technology automatically can efficiently extract valuable information from a variety of non-structured personal files, and carry out
Structured storage establishes talents information library, with easy-to-look-up and recycling.The present invention uses big data integrating means, will
The policy data of each " information island " is integrated, and proposes the policy semantic primitive abstracting method based on artificial rule to the talent
Policy realizes that key message extracts, and talents selection database is constructed, convenient for matching with talent's archive information.The present invention is based on
Talents information has been declared in the matching result in talents selection library and talents information library, filtering, realizes precisely push to the talent is not declared.
Detailed description of the invention
The present invention is described in further details below in conjunction with the drawings and specific embodiments；
Fig. 1 is the configuration diagram of a kind of talent towards file data and policy intelligent Matching system of the invention；
Fig. 2 is that the policy semantic primitive based on artificial rule extracts flow chart.
Specific embodiment
In order to solve the problems, such as the effective use of local archives papery talent's data, the present invention offers random field
(CRF) model realizes automatic extraction, the policy semantic primitive abstracting method based on artificial rule to extensive personnel record information
Talents selection key message is extracted, the talent for establishing automation and policy intelligent Matching system provide automatically for the talent
Policy Push Service.As shown in Figure 1, the invention also discloses a kind of talents towards file data and policy intelligent Matching system
System comprising with lower module:
Personal file identification module: for being editable electronic document by the personal file scanning recognition of papery and being formed
Personnel record information library；
Personal file property extracting module: the conditional random field models based on event extraction extract personnel record information library
Valuable information and structured storage formation talents information library；
Policy automatic collection module: current talents selection building talents selection database is obtained for acquiring；
Policy segments parsing module: the policy semantic primitive abstracting method based on artificial rule is to talents selection database
Talents selection key message is extracted to obtain quantifiable indicator information；
The talent and policy matching module: in the quantifiable indicator information and talents information library in talents selection database
Field matching and export the talent in all talent's information banks and policy matching result；
Policy pushing module: for rejecting from the high policy categories of matching result screening comprehensive matching degree score value with Shen
The talent of report pushes policy to the talent for registration and shows project information.
As shown in the figures 1 and 2, the invention discloses a kind of talent towards file data and policy intelligent Matching method,
Include the following steps；
Step 1, obtaining local talent's papery personal file and scanning is image, and will be scanned using image recognition technology
Image be converted into editable electronic document, form personnel record information library；
Further, the specific steps of step 1 are as follows:
Step 1.1, from talents market, office, people society, the acquisitions local such as archives talent's papery personal file, by papery occurrences in human life
The mode typing computer of archives scanning, saves as image；
Step 1.2, several subgraphs are divided the image into using OCR technique, includes an individual word in each subgraph
It is female；
Step 1.3, by subgraph from image format conversion at binary format, and by binary data transmission to BP nerve
Network；
Step 1.4, BP neural network finds out the association between character image data and numerical value by training process, will be swept
The image retouched is converted into editable electronic document, and recognition result enters system database, facilitates subsequent progress full text inspection
Rope.
Step 2, the big and non-structured personnel record information for quantity, using the condition random field based on event extraction
Model extraction valuable information and structured storage formation talents information library, facilitate the matching of the subsequent talent and policy.
Further, condition random field (CRF) model is a kind of sorter model that can be used for naming Entity recognition, this
Model regards the variety classes of information extraction as a kind of label for being directed to feature, thus by information extraction task be converted into
Determine the classification problem of text and its feature, feature used in condition random field (CRF) model is as shown in table 1.
1 condition random field of table (CRF) model occurrences in human life archive feature
The present invention carries out the specific steps of event extraction with conditional random field models to personnel record information are as follows:
Step 2.1, personnel record information archives text carry out word segmentation processing by document representation at tf/idf weight to
Amount,
Step 2.2, using based on document frequency method carry out feature extraction, filter out for correctly classify contribute it is low
Word；
Step 2.3, archives text training set is set, completes the conversion of archives text training set to characteristic set, and pass through
Artificial mark label learns tagsort；
Step 2.4, archives text test set is obtained into same type characteristic set, is then differentiated by sorter model
To corresponding tag along sort, carries out structured storage and establish talents information library.
Specifically, archives text generally includes personal essential information, education background, work experience and paper Patent Publication feelings
Condition.In different content blocks, item of information is listed with different modes, as education background content blocks include may be the time, learn
School, learned profession etc.；Paper publishing situation content blocks include may be author, thesis topic, the periodical delivered or meeting
Discuss collected works, deliver the time etc..On the other hand, the content blocks of different archives texts may be listed in a different order, most of
Archives text is personal essential information first, is next successively education background, work experience, is finally the patent feelings that publish thesis
Condition.The item of information listed in each content blocks is sequentially arranged, therefore these items of information can be counted as a system
The event of column, therefore event can be expressed as to a five-tuple:
E=<who, when, where, what, how>
Representation method building text feature and mark label based on above-mentioned event, mark label of the invention such as 2 institute of table
Show.Essential characteristic includes the features such as entity word, entity word part of speech, upper and lower cliction, upper and lower cliction part of speech, and mark label includes surname
Name, event bodies, time, place, the knots such as date of birth, address, phone, position, education experience/work experience/publication paper
Fruit etc..The personal file event information of condition random field (CRF) model treatment is subjected to structured storage, establishes talents information
Library.
2 condition random field of table (CRF) model personal file label
Step 3, the acquisition of policy automatic collection module obtains current talents selection and constructs talents selection database；Talent's political affairs
Plan acquisition modes include lead-in mode and extracting mode, and lead-in mode refers to government organs or the third-party institution actively by policy information
Editor's deposit talents selection database；Extracting mode refer to using robot, web crawlers, Web Spider cyber stalker
Automatic collection is carried out to policy informations associated with the talent all in selected target government website and is downloaded to local service
Device arranges deposit talents selection database again.
Step 4, policy segments policy semantic primitive abstracting method of the parsing module based on artificial rule to talents selection number
It is extracted to obtain quantifiable indicator information according to the talents selection key message in library；
Further, as shown in Fig. 2, step 4 specifically includes the building of policy dictionary, policy Text Pretreatment and policy information
Three steps are extracted, specifically:
The building of policy dictionary: in order to improve policy participle effect, need to construct talents selection dictionary and information extraction triggering
Vocabulary.Information of the building of talents selection dictionary based on local talents selection corpus, by policy type, policy title is applicable in
The vocabulary in corpus is included in condition, the classification such as keyword.Trigger word refers to that the extraction to a certain semantic primitive plays
Identification, mark effect, can trigger the word of extraction task.The building that information extraction triggers vocabulary is based on to local talents selection
The text feature and description habit of corpus are analyzed.Such as declare condition description generally will appear " satisfaction ", " meeting ",
Vocabulary such as " necessary ".
Policy Text Pretreatment: Text Pretreatment module mainly includes subordinate sentence, participle and part-of-speech tagging.Use the local talent
The title and text of policy corpus are segmented using Chinese Academy of Sciences ICTCLAS participle tool as experiment corpus and mark word
Property.
Policy information extracts: by the analysis to corpus to be extracted, formulating decimation rule based on triggering vocabulary, and using just
Then expression formula describes decimation rule and establishes rule base, finally carries out the extraction of the semantic primitive of local talents selection.By all political affairs
Plan information according to project name, type of subject, declare condition, declare economic welfare, policy link carry out data normalization arrangement
Talents selection database is imported afterwards.Feature Words extracting method in applicating text excavation declares item in talents selection database
Part carries out further semantic excavation, resolves into the index that can quantify to measure, is stored in talents selection database.
Step 5, the talent and policy matching module are by the quantifiable indicator information and talents information in talents selection database
Field in library carries out the talent and policy matching result in all talent's information banks of matching acquisition, wherein policy matching result packet
It includes project name, type of subject, project application condition, declare economic welfare and the notice link of newest policy.
Further, being carried out in step 5 using the matching process based on semantic matches rule will be in talents selection database
Quantifiable indicator information matched with the field in talents information library, such as " publishing thesis at least 5 " be equivalent to " paper number
Amount >=5 ", specifically includes the following steps:
Step 5.1, matching rule is from the easier to the more advanced sorted, is matched since the index being easiest to, returning to every can quantify
Targets match rules results；Matching rule is ranked up by matching complexity, first matches the index being easiest to, such as educational background, year
Age, the already present index such as the quantity that publishes thesis match the index not set up by keyword, and returning to every can measure
Change targets match rules results.
Step 5.2, each matching rule assigns different weights and already present index weights are greater than the index not set up,
And then comprehensive matching degree formula is constructed according to quantifiable indicator matching rule result, for measuring the matching of the talent and policy categories
Degree, matching degree include 5 grades: very matching (1 point), matching (0.8 point), comparison match (0.6 point), general matching (0.4
Point), mismatch (0.2 point).If talents information substantially conforms to all matching rules of the project, comprehensive matching similarity is close to 1
Point, then show that the talent can declare the project.
Step 5.3, auto-returned policy list of matches information after all policy categories is retrieved, comprehensive matching degree is pressed in list
Sequence, tabulating result include relevant project name, type of subject, project application condition, declare economic welfare and newest policy
Notice link.
Step 6, policy pushing module screens comprehensive after the talent and policy matching result returned in all talent's information banks
The high policy categories of matching degree score value are closed, are compared with project talents information library has been declared, to the policy categories that do not register
Carry out policy push in the form of mail, short message to the talent, show the declarable project name of the talent, project application economic welfare and
Newest policy notice link.
The invention adopts the above technical scheme, establishes policy automatic patching system based on local talent's resources bank.Pass through base
Papery talent archive information is identified in the OCR technique of BP neural network, it is automatic to match relevant policies for the local talent and go forward side by side administration
Plan casting push solves the problems, such as that talents selection matching takes time and effort.The present invention passes through the condition random field based on event extraction
(CRF) technology automatically can efficiently extract valuable information from a variety of non-structured personal files, and carry out
Structured storage establishes talents information library, with easy-to-look-up and recycling.The present invention uses big data integrating means, will
The policy data of each " information island " is integrated, and proposes the policy semantic primitive abstracting method based on artificial rule to the talent
Policy realizes that key message extracts, and talents selection database is constructed, convenient for matching with talent's archive information.The present invention is based on
Talents information has been declared in the matching result in talents selection library and talents information library, filtering, realizes precisely push to the talent is not declared.
Bibliography
[1] research [D] the Xi'an Polytechnic University of occurrences in human life resume intelligence extraction system of all wise man based on condition random field,
2016.
[2] personnel and post matching degree Calculating model and its application [J] Hubei University Of Technology journal in Zhu Pingli enterprise, 2009,
24(6):58-59.
[3] Zhang Xian, Liu Shengyan, Wang Wenguang personnel and post matching model construction and application: by taking the practice of company A personnel and post matching as an example
The exploitation of [J] Chinese human resources, 2014 (22): 54-60.
Claims (10)
1. a kind of talent towards file data and policy intelligent Matching method, it is characterised in that: it includes the following steps;
Step 1, obtaining local talent's papery personal file and scanning is image, and the figure that will be scanned using image recognition technology
As being converted into editable electronic document, personnel record information library is formed；
Step 2, valuable information is extracted simultaneously using the conditional random field models based on event extraction based on personnel record information library
Structured storage forms talents information library；
Step 3, acquisition obtains current talents selection and constructs talents selection database；
Step 4, the policy semantic primitive abstracting method based on artificial rule believes the talents selection key of talents selection database
Breath is extracted to obtain quantifiable indicator information；
Step 5, the quantifiable indicator information in talents selection database and the field in talents information library are subjected to matching acquisition
The talent and policy matching result in all talent's information banks,
Step 6, the talent Xiang Weibao to have declared is rejected from the high policy categories of matching result screening comprehensive matching degree score value
The talent of name pushes policy and shows project information.
2. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
The specific steps of step 1 are as follows:
Step 1.1, mode typing computer papery personal file scanned, saves as image；
Step 1.2, several subgraphs are divided the image into using OCR technique, includes an individual letter in each subgraph；
Step 1.3, by subgraph from image format conversion at binary format, and by binary data transmission to BP nerve net
Network；
Step 1.4, BP neural network finds out the association between character image data and numerical value by training process, by what is scanned
Image is converted into editable electronic document, and recognition result enters system database.
3. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
Step 2 carries out the specific steps of event extraction with conditional random field models to personnel record information are as follows:
Step 2.1, the archives text of personnel record information carries out the vector that word segmentation processing weights document representation at tf/idf,
Step 2.2, feature extraction is carried out using the method based on document frequency, filters out and contributes low list for correctly classifying
Word；
Step 2.3, archives text training set is set, completes the conversion of archives text training set to characteristic set, and by artificial
Mark label learns tagsort；
Step 2.4, archives text test set is obtained into same type characteristic set, then differentiates to obtain pair by sorter model
The tag along sort answered carries out structured storage and establishes talents information library.
4. a kind of talent towards file data according to claim 3 and policy intelligent Matching method, it is characterised in that:
The archives text of step 2.1 generally includes personal essential information, education background, work experience and paper Patent Publication situation.
5. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
The talents selection acquisition modes of step 3 include lead-in mode and extracting mode, and lead-in mode refers to government organs or the third-party institution
Policy information editor is actively stored in talents selection database；Extracting mode, which refers to, utilizes robot, web crawlers, Web Spider
Cyber stalker automatic collections are carried out simultaneously to associated with talent policy informations all in selected target government website
It is downloaded to local server and arranges deposit talents selection database again.
6. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
Step 4 specifically includes the building of policy dictionary, policy Text Pretreatment and policy information and extracts three steps, specifically:
The building of policy dictionary: including talents selection dictionary and trigger word, talents selection dictionary by by policy type, policy title,
The class condition of applicable elements and keyword includes the vocabulary of talents selection database；Trigger word is appointed for triggering to extract
Business, trigger word are the word for playing identification, mark effect to semantic primitive, and trigger word passes through to local talents selection corpus
Text feature and description habit carry out analysis extract obtain；
Policy Text Pretreatment: it is segmented using Chinese Academy of Sciences ICTCLAS participle tool and marks part of speech；
Policy information extracts: formulating decimation rule based on triggering vocabulary, and describes decimation rule using regular expression and establish rule
Then library finally carries out the extraction of the semantic primitive of local talents selection and is stored in talents selection database.
7. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
In step 5 using the matching process based on semantic matches rule carry out by talents selection database quantifiable indicator information and
Field in talents information library is matched, specifically includes the following steps:
Step 5.1, matching rule is from the easier to the more advanced sorted, is matched since the index being easiest to, return to every quantifiable indicator
Matching rule result；
Step 5.2, each matching rule assigns different weights and already present index weights are greater than the index not set up, in turn
The matching degree for obtaining the talent and policy categories is calculated,
Step 5.3, all policy categories are retrieved to return to all results and arrange by policy matching is formed after the sequence of comprehensive matching degree
Table.
8. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
The policy matching result of step 5 includes project name, type of subject, project application condition, declares economic welfare and newest policy
Notice link.
9. a kind of talent towards file data according to claim 1 and policy intelligent Matching method, it is characterised in that:
In step 6 policy push is carried out to the talent to the policy categories that do not register in the form of mail, short message and shows declarable project
Title, project application economic welfare and the notice link of newest policy.
10. a kind of talent towards file data and policy intelligent Matching system comprising with lower module:
Personal file identification module: for being editable electronic document by the personal file scanning recognition of papery and forming occurrences in human life
Archive information library；
Personal file property extracting module: the conditional random field models based on event extraction extract the valuable of personnel record information library
Value information and structured storage formation talents information library；
Policy automatic collection module: current talents selection building talents selection database is obtained for acquiring；
Policy segments parsing module: the talent of the policy semantic primitive abstracting method based on artificial rule to talents selection database
Policy key message is extracted to obtain quantifiable indicator information；
The talent and policy matching module: for the quantifiable indicator information in talents selection database and the word in talents information library
The matching of section simultaneously exports the talent in all talent's information banks and policy matching result；
Policy pushing module: for what is declared from the high policy categories rejecting of matching result screening comprehensive matching degree score value
The talent pushes policy to the talent for registration and shows project information.
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