CN108595708A - A kind of exception information file classification method of knowledge based collection of illustrative plates - Google Patents
A kind of exception information file classification method of knowledge based collection of illustrative plates Download PDFInfo
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Abstract
The present invention proposes a kind of exception information file classification method of knowledge based collection of illustrative plates, builds domain knowledge collection of illustrative plates first, constructs Entity recognition and entity link based on the domain knowledge collection of illustrative plates, then builds Text Representation vector vtextVector v is indicated with substance featureent, Text Representation vector and substance feature are finally indicated that vector splicing has been incorporated the new text representation vector v of knowledge featuremerge, classification based training is carried out to the new text representation vector, obtains final classification results.
Description
Technical field
The present invention relates to a kind of sorting technique more particularly to a kind of exception information text classification sides of knowledge based collection of illustrative plates
Method.
Background technology
With the development of internet and the continuous growth of the network information, the rapid development of network technology makes people to network
It increasingly relies on, along with ever-increasing information sharing and service propaganda on network, the safety problem of Web content has highlighted
Come.Therefore it is badly in need of the exception information recognition methods of a kind of high accuracy and strong autgmentability and provides network security for society and individual
Guarantee.
In the prior art, exception information detection there are two main classes method：One kind is that keyword is used to filter or with artificial
The mode that mode models exception information, it is artificial to formulate filtering keys word list matched text information；Another kind of is to be based on
The file classification method of statistics and machine learning, such as support vector machines, K neighbor algorithms and decision Tree algorithms.Above method takes
The effect obtained is all not satisfactory, and application scenarios limitation is often extremely difficult to balance between the accuracy and scalability of method.
Artificial formulation filtering keys word list, machinery are relied in such a way that exception information is identified in the method for keyword filtering
And autgmentability is poor, and the neologisms on network emerge one after another, and it can not be complete by exception information only according to artificial formulation lists of keywords
Covering, and can not also understand from the angle of semantic analysis and screen harmful information.Currently based on the Information Filtering Model of content
A large amount of rules manually formulated are relied on to complete to model, network harmful content is various informative, and the rule manually formulated can not sample
Sample exhaustion is to the greatest extent.In addition data mining technology and the neural network model of machine learning also obtain in terms of the identification of exception information
With application, but ignore the field priori involved in text, most methods are only started with from the surface characteristics of text, passed through
The word frequency of word or semantic vector carry out semantic modeling to text in text, can only simply utilize such as cooccurrence relation shallow-layer special
Sign, it is difficult to the semantic information for capturing the deeper contained in text, general character relationship, the inclusion relation of the things as mentioned by text
And the priori etc. of the unmentioned common sense property of text.
Currently, knowledge mapping has become the semantic interlink realized in big data analysis, the multi-source heterogeneous number in internet is realized
According to the important tool in the conversion of the specific things description to objective world.The foundation of knowledge mapping to the Unify legislations of data,
Effective integration, association are found and knowledge reasoning has established effective research method, knowledge mapping visualization technique Description of Knowledge
Resource and its carrier excavate, analysis, structure, draw knowledge and connect each other therebetween.As WordNet, DBPedia etc. are advised greatly
The appearance and development of mould knowledge base, a large amount of knowledge can be with Open Access Journals, and the knowledge feature obtained from knowledge base is also by increasingly
In more the applying to natural language processing of the tasks.As the natural language model based on neural network is embedded in (word by word
Embedding) text feature is carried out the success of vectorization expression by method, is achieved on the representation method of knowledge feature same
The remarkable result of sample, such as TransE to TranR a series of knowledge base entities and relationship embedding technique studies.But
A few class representation of knowledge learning methods of the prior art are mostly used for the knowledge bases field internal problems such as relation inference, link prediction,
And individually knowledge information is modeled mostly, it is not applied in exception information text identification.
Invention content
The present invention proposes a kind of exception information file classification method of knowledge based collection of illustrative plates, builds domain knowledge figure first
Spectrum, construct Entity recognition and entity link model based on the domain knowledge collection of illustrative plates, then build Text Representation to
Measure vtextVector v is indicated with substance featureent, Text Representation vector is finally indicated that vector splicing obtains with substance feature
The new text representation vector v of knowledge feature is incorporatedmerge, classification based training is carried out to the new text representation vector, is obtained
Final classification results.
The present invention is based on the Entity recognitions of knowledge mapping with linking and the short essay based on text Yu knowledge mapping union feature
This classification is detected to realize based on the short text exception information of text and knowledge mapping.Present invention introduces external knowledge library is auxiliary
Help the Deep Semantics excavation for carrying out text and character representation.Pass through entity relationship, classification, attribute etc. abundant inside knowledge base
The extraction for extending the Deep Semantics relationship in information support text passes through the entity disambiguation of knowledge based collection of illustrative plates and link method
The ambiguity problem for solving word handles the reference in text by the mapping relations of abbreviation complete in knowledge base and alias
Word finally adds to the knowledge base information for linking entity in the training process of model as supplemental characteristic, is realized to improve
The reliability of abnormal text classification.
Description of the drawings
Fig. 1 is the domain entities relationship system figure of one embodiment of the invention；
Fig. 2 is the attribute fusion of one embodiment of the invention and the qi flow chart that disappears；
Fig. 3 is the entity recognition model framework of the present invention；
Fig. 4 is the present invention based on text and knowledge mapping union feature classification process figure.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments,
The present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used to explain this hair
It is bright, it is not intended to limit the present invention.In addition, technology involved in the various embodiments of the present invention described below is special
Sign can be combined with each other as long as they do not conflict with each other.
As shown in Figure 1 for the exception information text detection embodiment of political class and concerning taxes class field, structure field is needed
Knowledge mapping establishes domain entities library.Building process is by extracting in news portal website, microblogging, wechat public platform and forum
Political, economic related data, and combine main a few class Chinese encyclopaedia website (Baidupedia, interactions hundred in current internet
Section, Chinese wikipedia) in semi-structured data supplement.Network data has polyphyly, derives from news portal net
It stands, multiple channels such as microblogging, wechat public platform and forum, different statement modes and data content knot is had in different platform
Structure, so needing to be handled the data of multi-source and realizing fusion.First using the rule-based tool that crawls from Network page
The data of structuring are extracted in face, and design simple rule and (filtering spcial character, conversion between simplified and traditional Chinese are cleared up to initial data
Deng) with normalization (unified time, the expression forms such as date), then will be obtained from Chinese encyclopaedia website with crawl with
Politics, economy class news, the relevant entry of microblogging are used as entity using original entry label in encyclopaedia website data
Simple K-means clustering algorithms are the delimitation classification of each entity, composition and classification system.About the not Tongfang of entry in encyclopaedia
Face description is used for constituting every attribute of entity, and the hyperlink in being described about entry in encyclopaedia can be used for establishing each reality
Isolated entity is connected into collection of illustrative plates by the incidence relation between body.It is preliminary based on having been obtained from multi-resources Heterogeneous data at this time
Molding knowledge mapping provides basis for follow-up work.
Since the expression-form of separate sources data differs with the quality of data, need to carry out knowledge fusion.Knowledge fusion packet
Containing entity alignment and two groundworks of qi that disappear of merging of attribute, entity alignment is retouched using physical name, entity class and entity
It states three dimensional characteristics and the list of entities that should be aligned is found out by Arithmetic of Semantic Similarity judgement, same entity is needed to be melted
The entity item attribute information of conjunction is organized into set, using attribute fusion as shown in Figure 2 and the qi flow that disappears, finally obtains complete
Solid data be stored in database.The building process of knowledge base in this way is basically completed, and storage medium uses neo4j chart databases,
The inquiry of the knowledge base of structure completion is carried out by way of the API Access interface that calling neo4j is provided.
Extract textual association to knowledge mapping information need through entity recognition method to the reality that is arrived involved in text
Pronouns, general term for nouns, numerals and measure words or phrase are labeled, and be linked in knowledge base its it is corresponding it is specific physically.The main task of Entity recognition is
The name such as the name mentioned in natural language text, place name, institution term Entity recognition is come out, and is optionally carried out real
The simple classification of body name.Current almost all of processing mode is all to regard this problem as the similar sequence labelling segmented to ask
Topic uses " BIO " labelling method that each word in sentence is marked, and " B " represents the beginning of some physical name, and " I " represents certain
The centre of a physical name or end, " O " represent the word other than physical name, reuse machine learning model mark data
It is trained on collection, such as condition random field (CRF) or Recognition with Recurrent Neural Network model.The present invention uses as shown in Figure 3
The binding model of BILSTM+CRF is first encoded text using shot and long term Memory Neural Networks (LSTM), each in text
Input of the term vector of word as LSTM, it is certain that then output, which is each word,
The groundwork of entity link is to find the corresponding entity in knowledge base according to name entity word, can be related to therebetween
And the disambiguation to entity of the same name, as judged, " Zhang San " should be linked to leader Zhang San in " Zhang San is a great Leader "
Physically or personage's biography Zhang San physically.The present invention is established generally by way of statistical learning under the data set of standard
Rate model completes the qi that disappears, and identifies the highest entity of probability, returns to entity id.The case where lacking the data set completely marked
Under, it first passes through knowledge library searching and enumerates all candidate entities, then utilize entity popularity, entity class and original text
The indexs such as this degree of association or the similarity of entity information and urtext carry out rule-based sequence and screening.
Traditional text representation method carries out vectorization expression by one-hot vectors or TF-IDF value sequences, right first
The word obtained after all text participles of data set is counted, and vocabulary is obtained after filtering low word stop words, if vocabulary size
For n, then the expression vector v of textd∈RnThe appearance feelings of i-th of word in the text in digital representation in i-th dimension vocabulary
Condition, 1 is to occur 0 not occur, or replace to obtain more preferable effect using the TF-IDF values of the word.But this representation method
Dimension can be brought excessively high, Sparse, and to the weaker equal prominent questions of the code capacity of Semantic Similarity.
To solve the above problems, the present invention uses word embedding grammar, single word is subjected to vectorization expression, by word
Between similitude be converted to the measurement of COS distance between vector.The present invention uses nearest 1 year news data and Chinese
Wikipedia data carry out term vector training using word2vec.
Text is considered as the sequence of terms (w occurred successively1, w2, w3...), if word wiWord2vec vector tables
It is shown as vwi, vector length k is stitched together the term vector of all words of text to obtain Text Representation vector vtext∈
Rs×k。
The main target of knowledge mapping be various entities and concept existing for describe among real world and it
Between incidence relation.Knowledge mapping is by " entity-relationship-entity " triple, by entity with the real world and concept
It is mapped in a semantic network, can effectively solve the problem that the low density problem of open the Internet big data information value, it is especially suitable
Relevant information retrieval task related for entity, semantic.But entity relationship is very difficult to apply in Algorithm of documents categorization, this hair
The bright semantic information by knowledge base is expressed as dense low-dimensional real-valued vectors, towards in knowledge mapping entity and relationship carry out
Indicate study.
The present invention indicates entity and relation vector using TransE models, by each triple example (head,
Relation, tail) in relationship relation regard the addition of vectors from entity head to entity tail as, by constantly adjusting
The vector of whole head, relation and tail keep (h+r) equal with t, i.e. h+r=t.
By TransE algorithms in knowledge mapping entity and after relationship is indicated study, each entity and pass
System can indicate v with a k dimensional vectorei.Then it can show that the vector of knowledge feature indicates：The reality of knowledge based collection of illustrative plates
Method body identification and linked, text (w1, w2, w3...) can the entity that arrives of text link be (e1,e2,……,et),
The entity vector of all entities is stitched together to obtain substance feature vector expression vent∈Rt×k。
Text Representation vector and substance feature are indicated that vector splicing has been incorporated the text representation of knowledge feature
Form：
With new text representation vector vmergeInstead of the expression vector v originally based on plain text featuretext, participate in mesh
Model training in, to complete to expand the feature of target text, increase the support to Deep Semantics information, improve model
Quality and completeness.
The disaggregated model of the present invention is illustrated in figure 4 to vmergeCarry out classification based training.Using CNN deep learnings model into
Row classification based training, vmergeVector is spliced into the representing matrix of text, CNN layers is input to, result is finally input to full connection
Network classifier carries out model training, obtains final classification as a result, ensure that capture of the model to text Deep Semantics information,
Improve classification quality and reliability.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：It is still
It can modify to the technical solution recorded in previous embodiment or equivalent replacement of some of the technical features；
And these modifications or replacements, the spirit for various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
And range.
Claims (6)
1. a kind of exception information file classification method of knowledge based collection of illustrative plates, which is characterized in that domain knowledge collection of illustrative plates is built first,
Entity recognition and entity link based on the domain knowledge collection of illustrative plates are constructed, Text Representation vector v is then builttextWith
Substance feature indicates vector vent, Text Representation vector is finally indicated that vector splicing has been incorporated with substance feature and is known
Know the new text representation vector v of featuremerge, classification based training is carried out to the new text representation vector, obtains final point
Class result.
2. the method as described in claim 1, which is characterized in that the structure domain knowledge collection of illustrative plates is by extracting in different platform
Multi-source data handled and merged, establish between entity class and entity and be associated with, then carry out knowledge fusion；It is described to know
It includes entity alignment and two steps of qi that disappear that merge of attribute to know fusion, and physical name, entity are used in the entity alignment step
Three dimensional characteristics of classification and entity description find out the list of entities that should be aligned by Arithmetic of Semantic Similarity judgement, will be same
All entity item attribute informations to be fused of entity are organized into set.
3. the method as described in claim 1, which is characterized in that the structure is passed through based on the Entity recognition based on collection of illustrative plates
Using the binding model of BILSTM+CRF, first text is encoded using LSTM algorithms, the term vector of each word in text
As the input of LSTM, then output is the probability that each word is some label, and as the input of CRF, randomization transfer
Probability matrix finds out the highest flag sequence of probability according to deduction algorithm；The chain of entities based on collection of illustrative plates based on described is built to connect
The mode for crossing statistical learning establishes probabilistic model under the data set of standard, completes the qi that disappears, identifies the highest entity of probability, return
Return entity id.
4. the method as described in claim 1, which is characterized in that the structure Text Representation vector uses word insertion side
Single word is carried out vectorization expression, the similitude between word is converted to the measurement of COS distance between vector, led to by method
It crosses neural network to learn text feature, while reducing term vector dimension；If word wiWord2vec vectors be expressed as
vwi, the term vector of all words of text is stitched together to obtain Text Representation vector by vector length kThe s is the quantity of word, the vtext∈Rs×k。
5. method as claimed in claim 4, which is characterized in that it is to pass through to build the method that the substance feature indicates vector
TransE algorithms in knowledge mapping entity and relationship be indicated study, one k dimensional vector of each entity and relationship
Indicate vei, text (w1, w2, w3...) can the entity that arrives of text link be (e1,e2,……,et), by the reality of all entities
Body vector is stitched together to obtain the substance feature expression vectorThe t is real
The quantity of body, vent∈Rt×k。
6. method as claimed in claim 5, which is characterized in that Text Representation vector and substance feature are indicated that vector is spelled
Connect the new text representation vector v for having been incorporated knowledge featuremergeMode beThen make
Classification based training is carried out with CNN deep learnings model, by vmergeVector is spliced into the representing matrix of text, is input to CNN layers, finally
Result is input to fully-connected network grader progress model training and obtains final classification result.
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