CN102033950A - Construction method and identification method of automatic electronic product named entity identification system - Google Patents
Construction method and identification method of automatic electronic product named entity identification system Download PDFInfo
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Abstract
The invention discloses a construction method and an identification method of an automatic electronic product named entity identification system, relates to a construction method and an identification method of a named entity identification system in natural language processing and belongs to a technique for automatically identifying names of electronic products from related information. The invention is used for identifying the names of the electronic products and solves the problems that a rule-based identification system has low recall rate during identification and a machine learning-based identification system needs to manually label a great deal of training language database during identification. The construction method comprises the following steps of: forming a knowledge base of the original linguistic data; constructing a label language database; and performing electronic product named entity identification on the basis of a conditional random field method. The identification method comprises the following steps that: a free text is input into the automatic electronic product named entity identification system; and the system extracts characteristics by using a characteristic template, acquires each weight corresponding to each characteristic by using a conditional random field model and calculates the weights by the conditional random field method to acquire an identification result.
Description
Technical field
The present invention relates to the construction method and the recognition methods of the named entity recognition system in the natural language processing, belong to the technology of from relevant information, the title of electronic product being discerned automatically.
Background technology
Outwardness and the things that can distinguish mutually are referred to as entity.Entity can be concrete people, thing and thing, also can be abstract concept or contact.The named entity recognition task is meant the entity that has certain sense in the identification text.Along with human society steps into digital Age, increasing electronic product has entered into people's life.Various reports about electronic product appear in the electronic document in a large number.Advertisement, using method and user comment have been full of on the internet especially about electronic product.Electronic product named entity recognition technology can help people better inquiry and the own interested electronic product information of management, help enterprise to find interconnected user on the network quickly to the feedback of own product and deliver advertisement more accurately, so this technology more and more receive the concern of industry member and academia.
Present named entity recognition technology is primarily aimed at these traditional named entities such as name in the news language material, place name, mechanism's name.Major technology can be divided into two classes: rule-based technology and based on the technology of machine learning.Rule-based technology is mainly utilized the composition rule of named entity, adopts the mode of artificial constructed knowledge base and rule base to carry out Entity recognition, and this technology accuracy rate is higher, but recall rate is lower, and is difficult to transplant.Technology based on machine learning mainly adopts machine learning algorithm and contextual feature to carry out Entity recognition, and wherein important recognizer comprises Hidden Markov Model (HMM), maximum entropy model, supporting vector machine model, decision-tree model etc.These class methods need a large amount of corpus of artificial mark, for lacking the common poor effect of electronic product named entity that marks language material.
Compare with traditional named entity, the electronic product named entity has following characteristics: it is faster 1) to upgrade variation; 2) constitute complicatedly, and mix usually and a large amount of numerals is arranged and stride language character; 3) lack standard and mark language material.The research for the electronic product named entity recognition at present both at home and abroad still is in the starting stage, and each sticks to his own version or argument to the definition of electronic product named entity.And corresponding recognition method also mainly concentrates on and directly applies mechanically traditional named entity recognition technology, lacks specific aim, so recognition accuracy and recall rate all are difficult to reach realistic scale.
Summary of the invention
The construction method that the purpose of this invention is to provide a kind of electronic product named entity automatic recognition system, with solve rule-based recognition system when identification recall rate lower, and need the problem of a large amount of corpus of artificial mark during based on the recognition system identification of machine learning.
It comprises the steps: one, utilizes downloaded software to collect the electronic product info web of multiple type from the internet, extracts the text of info web, thereby forms the knowledge base of original language material; Use participle part-of-speech tagging instrument, original language material is carried out participle and part-of-speech tagging processing, according to the definition of electronic product named entity, the language material behind participle and the part-of-speech tagging is carried out the entity mark afterwards, make up a tagged corpus; Described definition to the electronic product named entity is meant that brand name according to an electronic product named entity, series name and model three parts distinguish the electronic product named entity; Two, based on condition random territory method, define a plurality of feature templates, feature templates utilization mark language material and knowledge base specifically dissolve feature, the operation result of condition random territory method on tagged corpus can be given certain weight for each feature, and the condition random domain model that feature and its corresponding weight constitute just can be used for carrying out the electronic product named entity recognition.
The present invention also provides the recognition methods based on above-mentioned electronic product named entity automatic recognition system, and it comprises the steps: one, the free text that is used to discern is imported described electronic product named entity automatic recognition system; Two, system at first utilizes feature templates to extract feature, utilizes the condition random domain model to obtain the weight of each feature correspondence then, utilizes these weights condition random territory method to carry out computing and just obtains final recognition result.
Method of the present invention uses participle part-of-speech tagging instrument to come the electronic product info web of collecting in the internet is handled, avoided by a large amount of corpus of artificial mark, handle free text based on condition random territory method and knowledge base, tagged corpus, so recognition system recall rate height when identification.The commercial matters information of magnanimity on the internet can be managed and organize to method of the present invention effectively, and the raising people search, the efficient of management and use information.The present invention utilizes the composing law of electronic product named entity, change characteristics fast, various informativeization at product class named entity, electronic product named entity recognition method based on the condition random domain model of knowledge base has been proposed, and having realized corresponding system, the accuracy rate and the recall rate of system identification all reach more than 86%.
Description of drawings
Fig. 1 is the schematic flow sheet of embodiment of the present invention five, and Fig. 2 is the example schematic of an identification in the embodiment five.
Embodiment
Embodiment one: the construction method of the electronic product named entity automatic recognition system of present embodiment comprises the steps: one, utilizes downloaded software to collect the electronic product info web of multiple type from the internet, extract the text of info web, thereby form the knowledge base of original language material; Use participle part-of-speech tagging instrument, original language material is carried out participle (spliting between speech in the sentence and speech) and part-of-speech tagging processing (marking the part of speech of each speech), afterwards according to the definition of electronic product named entity, language material behind participle and the part-of-speech tagging is carried out the entity mark, make up a tagged corpus; Described definition to the electronic product named entity is meant that brand name according to an electronic product named entity, series name and model three parts distinguish the electronic product named entity; Two, based on condition random territory method, define a plurality of feature templates, feature templates utilization mark language material and knowledge base specifically dissolve feature, the operation result of condition random territory method on tagged corpus can be given certain weight for each feature, and the condition random domain model that feature and its corresponding weight constitute just can be used for carrying out the electronic product named entity recognition.
Embodiment two: the difference of present embodiment and embodiment one is: the resource in the knowledge base all utilizes web crawlers technology and information extraction technique to obtain automatically from the internet; Described knowledge base comprises: have the brand name dictionary that the brand message characteristic is constructed at electronic product; The series name dictionary that the branch structure of series is arranged at the electronic product under the brand; Or has the particular words knowledge base that the phrase of certain sense is constructed at some.
Embodiment three: the recognition methods based on the electronic product named entity automatic recognition system of embodiment one of present embodiment, it comprises the steps: one, the free text that is used to discern is imported described electronic product named entity automatic recognition system; Two, system at first utilizes feature templates to extract feature, utilizes the condition random domain model to obtain the weight of each feature correspondence then, utilizes these weights condition random territory method to carry out computing and just obtains final recognition result.
Embodiment four: the difference of present embodiment and embodiment three is: it also comprises step 3, adopts regular modification method that the electronic product named entity after discerning is revised, and described modification rule is by obtaining based on wrong method of driving.
Embodiment five: below in conjunction with Fig. 1 and Fig. 2, technical solution of the present invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making all other embodiment that obtained under the creative work prerequisite.
Present embodiment provides the automatically method of identification of a kind of electronic product named entity, can find automatically with the free text of recognition network in the associated electrical product naming entity.Specify embodiments of the present invention below in conjunction with Fig. 1.Present embodiment comprises: the 1) structure of corpus and knowledge base; 2) structure of feature extraction and feature templates; 3) will be applied in the electronic product named entity recognition based on the method that the machine learning and the rule of knowledge base are revised.
(1) definition of electronic product named entity and corpus make up.
In the present invention,, product naming entity is divided into three parts according to the difference of product component renewal frequency, i.e. the brand of product (BRA), series (SER), model (TYP), and product naming entity is described attribute by these three assemblies and some and is formed.Wherein, brand refers to intrinsic noun---the trade mark of product, for example " Nokia "; Series name is meant a series of under the brand, and for example " EasyShare " is exactly a series under Kodak's digital camera brand; Model name refers to the version information under brand or the series, is made up of letter, numeral and some symbols, and for example " Nokia N70 " middle N70 is exactly a model under the Nokia product brand.Usually, the highest part of renewal frequency is a product type in the digital product field, and in definition of the present invention, model name will be discerned as an independent classification, after the method that combines statistics and rule, this classification can effectively be discerned, and has higher renewal tolerance and accuracy of identification.
In addition, in actual applications, brand name and exabyte are born the same name, and this may produce ambiguity.For example " Nokia has released the mobile phone of a novel fashion recently---the N70 of Nokia.”。First " Nokia " refers to Nokia in the statement, and second " Nokia " then represents brand.The brand name that also has not only can be born the same name with exabyte, and under particular environment, it represents other classifications, and for example " apple " can be brand name, exabyte, also can be fruit, and this just need determine the implication that such speech is correct by contextual environment.That is to say to have only the name part of speech structure of working as to contain following determinacy product information, just can constitute product naming entity.
1) contain product brand, series or model entity any one, two or three, as " Nokia 5800 ", " 6300 mobile phone " is a ProductName entity, " Nokia " is the brand name entity, " 6300 " are the model name entities, and " digital camera " then is not a ProductName entity;
2) although do not contain brand, series or type information, contain peculiar product another name of certain brand or version information, be a ProductName entity as " little black notebook ", because " little black " is the another name of Thinkpad series under the association.And " intelligent " is the attribute information that all brands can have in " intelligent mobile phone ", so it is not a ProductName entity.
On the basis of above-mentioned electronic product named entity definition, utilize web crawlers from the internet, to collect the electronic product network information of multiple type, the text that extracts info web is built into original corpus.Utilize brand, serial knowledge base and participle part-of-speech tagging instrument then, original language material is carried out participle and part-of-speech tagging processing, according to the definition of electronic product named entity, the language material behind participle and the part-of-speech tagging is carried out the entity mark afterwards.After processing is handled to original corpus, use correlation technique that the processing corpus is carried out the consistance evaluation and test again, language material not up to standard is marked again, acquire a certain degree up to evaluation metrics.Through above-mentioned steps, made up one contain much information, standard corpus that the text type is various.
(2) based on the electronic product named entity recognition method of the condition random domain model of knowledge base.
Named entity recognition is extremely important for the aftertreatment of text message.In named entity recognition, the most frequently used machine learning method has maximum entropy model, the latent markov model of maximum entropy and condition random domain model.This three class model has closely similar common ground, belongs to the discriminant model.Used the best condition random domain model of effect among the present invention.Machine learning model all needs to make up a feature templates, is used for extracting when the current speech of identification the contextual feature of this speech.Therefore, the structure of feature templates is particularly important.
In the present invention, the process of ProductName Entity recognition is seen as a polytypic process, the target classification that wherein needs to discern comprises product brand name, series name, model name, reaches exabyte and product entity, the name entity of each classification is subdivided into beginning part, center section and whole three kinds of situations again, all is classified as a class not belonging to word any in the above classification.In order to solve the difficult problem that triggers of candidate's entity in the product naming entity identification, introduced product brand storehouse and serial storehouse trigger condition, simultaneously the identification of the triggering model name entity that they also can be covert as brand entity and serial entity.Feature in the model is produced by a series of feature templates.Defined 13 monobasic feature templates altogether, utilized the mode of these 13 monobasic feature templates that define then, be built into the set of binary feature template, carried out the screening of binary feature template then with information gain by making up in twos.Screening is made up in twos with monobasic feature templates and binary feature template after finishing, and is built into the ternary feature templates, uses the same method and screens, and has screened more than 40 feature templates construction feature template set at last.
After setting up the feature templates set, from the standard corpus of mark, select a certain proportion of language material composing training set at random, utilization feature templates collection and machine learning algorithm are trained, and by the adjustment to frequency of training, make the good match actual text of model energy that trains.Use the rule learning algorithm again, analyze the experimental result mistake, extract the rule that corrects mistakes, the improvement system is to the performance of electronic product named entity recognition.
More than electronic product named entity automatic identifying method and system that the embodiment of the invention provided are described in detail, used specific case herein principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (4)
1. the construction method of electronic product named entity automatic recognition system, it is characterized in that it comprises the steps: one, utilizes downloaded software to collect the electronic product info web of multiple type from the internet, extract the text of info web, thereby form the knowledge base of original language material; Use participle part-of-speech tagging instrument, original language material is carried out participle and part-of-speech tagging processing, according to the definition of electronic product named entity, the language material behind participle and the part-of-speech tagging is carried out the entity mark afterwards, make up a tagged corpus; Described definition to the electronic product named entity is meant that brand name according to an electronic product named entity, series name and model three parts distinguish the electronic product named entity; Two, based on condition random territory method, define a plurality of feature templates, feature templates utilization mark language material and knowledge base specifically dissolve feature, the operation result of condition random territory method on tagged corpus can be given certain weight for each feature, and the condition random domain model that feature and its corresponding weight constitute just can be used for carrying out the electronic product named entity recognition.
2. the construction method of electronic product named entity automatic recognition system according to claim 1 is characterized in that the resource in the knowledge base all utilizes web crawlers technology and information extraction technique to obtain automatically from the internet; Described knowledge base comprises: have the brand name dictionary that the brand message characteristic is constructed at electronic product; The series name dictionary that the branch structure of series is arranged at the electronic product under the brand; Or has the particular words knowledge base that the phrase of certain sense is constructed at some.
3. the recognition methods of the electronic product named entity automatic recognition system that makes up based on the described method of claim 1 is characterized in that it comprises the steps: one, the free text that is used to discern is imported described electronic product named entity automatic recognition system; Two, system at first utilizes feature templates to extract feature, utilizes the condition random domain model to obtain the weight of each feature correspondence then, utilizes these weights condition random territory method to carry out computing and just obtains final recognition result.
4. electronic product named entity automatic identifying method according to claim 3, it is characterized in that it also comprises step 3, adopts regular modification method that the electronic product named entity after discerning is revised, described modification rule is by obtaining based on wrong method of driving.
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| CN102609853A (en) * | 2012-02-27 | 2012-07-25 | 蒋永 | System and method for intelligently identifying names and models of commodities |
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| CN103164426A (en) * | 2011-12-13 | 2013-06-19 | 北大方正集团有限公司 | Method and device of recognizing named entity |
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Application publication date: 20110427 |