CN101373532A - FAQ Chinese request-answering system implementing method in tourism field - Google Patents
FAQ Chinese request-answering system implementing method in tourism field Download PDFInfo
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Abstract
The invention provides an implement method of an FAQ Chinese question answering system in the tourism field. The implement method comprises the steps of FAQ collection and organization, construction of a tourism field knowledge base, user query, question analysis, answer extraction and the like, thereby realizing the FAQ Chinese question answering system in the tourism field. The implement method constructs the tourism field knowledge base-field knowledge network with the help of the idea of ontology, utilizes the KDML language to define and describe the terms and the relations of the tourism field and realizes the integration of the tourism field knowledge network and a general knowledge base-knowledge network. The invention proposes a calculation method of similarity of tourism questions on the basis, the method realizes the calculation of the similarity of the questions with the help of the characteristics of the questions of the tourism field and the combination of morphological relations, the syntactic dependency relations and the field concept semantic relations in the questions, and the method further searches the related question from a candidate question set and extracts the answer of the question based on the similarity calculation. The test result of a Yunnan tourism FAQ question answering system proves that the method is feasible and has better effect.
Description
Technical field
The present invention relates to a kind of tour field FAQ Chinese question answering system implementation method, especially a kind of question answering system implementation method based on tour field frequently asked question storehouse (FAQ) belongs to artificial intelligence field.
Background technology
Frequently asked question FAQ (Frequently-asked Question) is the main means that online help is provided on the current network, answers questions by organizing some possible often asking in advance, is distributed on to be user's service of providing advice on the webpage.The FAQ knowledge organization is simple, easy to maintenance, but, accumulation gradually along with the frequently asked question collection, problem quantity is increasing, the knowledge acquisition approach of browsing formula page by page will more and more be difficult to satisfy user's actual demand, will waste a large amount of quality time of user, in addition when user capture just find not have the own information that really needs, time and effort consuming during all-links at all.
Summary of the invention
Purpose of the present invention provides a kind of tour field FAQ Chinese question answering system implementation method for addressing the above problem just, with efficiently, is user's service of providing advice quickly and accurately.
The present invention finishes by following technical proposal: a kind of tour field FAQ Chinese question answering system implementation method is characterized in that comprising:
(1) FAQ collects and tissue: combining artificial or semi-automatic mode, extract the tourism question and answer from the internet right, and arrangement enters tourism question and answer storehouse, forms the FAQ storehouse of travelling;
(2) tour field construction of knowledge base: make up and safeguard the tour field structure of knowledge and relation, form the tour field knowledge base;
(3) user inquiring: on the internet, the user carries out the travel information inquiry by natural language problem;
(4) case study: the problem to user input is analyzed, and it is interdependent to information such as, problem typeses to extract keyword, expansion word, the sentence structure of sign problem;
(5) answer is extracted: according to the case study result, from frequently asked question storehouse (FAQ), put forward retrieval candidate problem, adopt the field question similarity calculating method, calculate customer problem and candidate problem similarity, the problem answers that extracts the similarity maximum is as the candidate answer, and offer the user, return the final user and inquire about answer; The user can provide the natural language problem towards text, and system directly returns answer, rather than a large amount of webpages relevant with problem.
Described step (1) FAQ collects with method for organizing and is specially: climb automatically from the internet by web crawlers for first kind and get, and enter the FAQ storehouse by artificial screening; Second kind is by artificially collecting and put in order acquisition, at tour field, the special relevant issues such as relevant introduction, admission ticket, traffic such as place, sight spot, local conditions and customs, hotel of collecting, taxonomic revision with organize the FAQ question and answer to and enter the FAQ storehouse; The third then is a non-existent new question sentence by system's automatic recording user input but in the question sentence storehouse, and this class question sentence unification is saved in the question and answer history library, regularly by the manual examination and verification arrangement, the answer and the question sentence of correspondence is gone into the FAQ storehouse together.
The right storage of the question and answer of described FAQ is by setting up problem (question) and two relation tables of answer (answer), and (Questionid Answerid) carries out the answer index by major key respectively; The storage of issue table, for the ease of quick retrieval, adopt the inverted index mode to store, set up the inverted index document between speech and the question sentence, the selection of candidate question set is extracted from index file, and final result is then according to the answer answerid that deposits in the issue table directly output from database.
Described step (2) has mainly been utilized ontological thought, carries out the expansion of field term by " knowing net " of language message center Mr. Dong Zhendong of institute of the existing Chinese Academy of Sciences.
Described step (2) is specially: by ontological thought the notion of yunnan tourism field resource is accurately described, based on " knowing net ", adopt the conceptual description language KDML rule of " knowing net ", set up special yunnan tourism domain body, form resource ontology library field, yunnan tourism field and know dictyosome system, and realized the fusion that net " is known " in yunnan tourism knowledge base " net is known in the field " and general knowledge storehouse.Altogether relevant 2012 notions of tourisms such as the introduction of yunnan tourism sight spot, local conditions and customs, tourist communications are defined and describe at present, formed the yunnan tourism field and " known net ".
Because with respect to open field, be subjected to confinement to have certain domain knowledge characteristics, can reduce the difficulty of natural language processing by means of the domain knowledge relation.Ontology (Ontology) is a kind of accurate description to notion, particularly to the description of field concept, is that a kind of good domain knowledge is represented mode.Body is described the ABC architecture of delineating out a certain specific area by the standardization to notion, term and mutual relationship thereof.HowNet is a general general knowledge resource " to know net ", and it has described the notion of the word representative of Chinese and english, discloses between notion and the notion and attribute that notion had and the relation between the attribute.By ontological thought the notion of tour field resource is accurately described, based on " knowing net ", adopt the conceptual description language KDML rule of " knowing net ", set up special tour field body, form tour field resource ontology library field and know dictyosome system, and realized the fusion that net " is known " in tourism knowledge base " net is known in the field " and general knowledge storehouse.Altogether relevant 2012 notions of tourisms such as the introduction of yunnan tourism sight spot, local conditions and customs, hotel, tourist communications are defined and describe at present, form the tour field knowledge base.
Described step (3) is specially: the user can adopt the natural language mode to inquire about, and directly obtain the answer of problem by be correlated with tour field problem inquiry of internet.
Described step (4) is specially: by the question sentence analysis module problem of user's input is analyzed, mainly comprise lexical analysis, syntactic analysis and semantic analysis, lexical analysis is mainly carried out participle to problem, filter stop words, extract the inquiry core word and carry out keyword expansion by " knowing net "; Syntactic analysis is mainly to extracting the core stem of question sentence, and by Harbin Institute of Technology's parser, it is interdependent right to extract the question sentence sentence structure; The question sentence semantic information is mainly analyzed in semantic analysis, and according to the tour field characteristics, tour field problem types characteristic sentence mould rule is also extracted in definition, and realizes the identification of question sentence type by the rule match mode, specifically:
A, key to the issue speech, expansion word extract:
Key to the issue speech and expansion word are the fundamentals of sign problem, also are the bases that candidate question set retrieval and answer are extracted.By the load domains knowledge base, after to the question sentence participle, carry out a field term cutting again, realize field term cutting and part-of-speech tagging, and utilize " knowing net ", keyword is carried out the synonym expansion, form the keyword expansion speech.
B, question sentence sentence structure are interdependent to extracting:
Because complete Chinese sentence is by the trunk composition of sentence and is modified into branch and constitutes, and people often just can understand the general meaning of a sentence from the trunk composition, because there are a lot of difficulties in syntactic analysis fully at present, so when carrying out sentence similarity calculating, can embody similarity between the sentence by the right similarity of effectively arranging in pairs or groups between the sentence.So-called effectively collocation is right to being meant full sentence core word and directly existing with ... the collocation that its effective speech forms, and effectively speech is defined as verb, noun and adjective here, and it is to be determined by the part-of-speech tagging behind the participle.And the shared language technology platform LTP interface by Harbin Institute of Technology obtains sentence, and effectively collocation is right.
C, the classification of tour field question sentence
Problem types is the extract key factor of answer strategy of location answer and formulation, when question sentence is analyzed, judge whether two problems are similar, whether the problem types that at first must judge two problems is consistent, if it is consistent, just can carry out further similarity and calculate judgement, otherwise two problems can not be similar.In specific area, because professional relative fixed, therefore question sentence inquiry content-form is limited relatively, therefore can classify to problem at the question and answer business, improve the accuracy rate of similar question sentence retrieval and answer extraction with this, at tour field, extract the composition rule that has made up the variety of issue type, and by these feature identification problem typeses.
Described step (5) answer extracting method is specially: question sentence relation information such as the problem types that obtains according to the problem branch, key to the issue speech, problem expansion word, adopt lucene from frequently asked question storehouse (FAQ), to retrieve the candidate problem set, by the field question similarity calculating method, the candidate problem answers that extracts the similarity maximum is as answer, and return to the user, specifically:
The selection of A, candidate question set and question sentence index
The purpose of setting up candidate question set is to dwindle seek scope, and comparatively complicated process such as follow-up similarity calculating is all carried out in this relative small range of candidate question set, thereby improves the recall precision of system.In tourism (FAQ) question answering system, adopt the Lucene searching system to realize retrieval of candidate's problem and question sentence index.
B, tour field question sentence similarity are calculated
It is the basis that similar question sentence is searched among the FAQ that the question sentence similarity is calculated, it simultaneously also is the key that answer is extracted, it directly influences the order of accuarcy that answer is extracted, current existing multiple Chinese sentence similarity computing method are divided into Three Estate: grammer similarity, semantic similarity and pragmatic similarity usually.But these similarity calculating methods all have different defectives in the question answering system of field (FAQ).At this in conjunction with tour field question sentence characteristics, take all factors into consideration semantic distance, sentence structure dependence and the field concept semantic relation factor of speech, a kind of question sentence similarity calculating method has been proposed, this method is at first utilized the field question characteristics, carry out similar problem according to problem category and filter, reach " net is known in the field " knowledge base based on " knowing net " then, it is effectively interdependent right to adopt syntactic analysis to extract question sentence, and utilize interdependent to the notion semantic relation, realize that similarity is calculated between the tour field question sentence.The calculating committed step is as follows:
(1) semantic similarity of speech calculates
Speech is the basis of forming sentence, and sentence similarity must show by the similarity that sentence comprises between the speech, and computing method are with reference to the phrase semantic similarity calculating method of Liu Qun based on " knowing net ", and computation process is as follows
Wherein, Sim
1(C
1, C
2) be first independent adopted former similarity, the Sim of notion
2(C
1, C
2) be former similarity, the Sim of other independent justice
3(C
1, C
2) for concerning adopted former similarity and Sim
4(C
1, C
2) be the former similarity in Signifier, β i (1≤i≤4) is an adjustable parameter, and: β
1+ β
2+ β
3+ β
4=1, β
1〉=β
2〉=β
3〉=β
4, β
1〉=0.5.
(2) the question sentence sentence structure is interdependent calculates similarity
The sentence structure that can extract question sentence according to the problem syntactic analysis is interdependent right, the effective collocation that has obtained two question sentences in extraction to after, must be relatively two question sentences effectively collocation between similarity, effectively arrange in pairs or groups between question sentence to similarity in order to calculate, at first must calculate two effectively collocation between similarity relation between the equivalent, according to knowing net disambiguation annotation results, employing is based on " knowing net " Word similarity method (A step), calculate interdependent similarity respectively, get two similar mean values then and obtain two effective interdependent right similarities last two corresponding words.Calculate similarity between the question sentence according to interdependent right similarity then, for any two question sentence A and B, the question sentence sentence that A comprises is interdependent to being A
1, A
2..., A
m, the question sentence sentence that B comprises is interdependent to being B
1, B
2..., Bn, at first interdependent to being benchmark with among the question sentence A calculated interdependent to A
i(1≤i≤m) and B
j(similarity S (the A between 1≤j≤n)
i, B
j), select S (A successively according to formula (2)
i, B
j) maximum interdependent right, interdependent to be sky in the A sentence picked out an effective interdependent pair set { a of sentence
i, interdependent to being benchmark with the effective sentence of B equally, it is interdependent to B to calculate sentence
j((similarity between 1≤i≤m) is expressed as S (B for 1≤j≤n) and Ai
j, A
i), select S (B successively according to formula (3)
j, A
i) maximum interdependent right, interdependent to be sky in the B sentence picked out an effective interdependent pair set { b of sentence
j.
a
i＝max(S(A
i，B
1)，S(A
i，B
2)，Λ，S(A
i，B
n)) (2)
b
j＝max(S(B
j，A
1)，S(B
j，A
2)，Λ，S(B
j，A
m)) (3)
(3) tour field question sentence similarity is calculated
Judgement of question sentence type and question sentence are interdependent to after extracting carrying out, just can be according to interdependent two interdependent pair set { a that obtain that similarity is calculated
i, { b
jCarry out the calculating of question sentence similarity, computing formula is as follows:
Adopt that effective sentence structure is interdependent to be calculated carrying out the question sentence similarity, reduce the complexity of algorithm greatly, accuracy rate can obtain raising to a certain degree.This method is considered from the shallow-layer sentence structure, has considered the dependence between speech and the speech, and is more abundant to the understanding of sentence, thereby can obtain the value of sentence similarity more accurately.
The retrieval of C, similar problem and answer are extracted
After determining candidate question set, further from Candidate Set, pick out the question sentence the most similar exactly to the target question sentence.The thought of similar question sentence retrieval is the similarity between each question sentence and the target question sentence in the calculated candidate problem set, get similarity greater than the question sentence of the assign thresholds λ of system as similar question sentence, and according to the answer of this question sentence correspondence numbering (AnswerId), Automatic Extraction goes out relevant answer and returns to the user as the output result from database.According to tour field question sentence characteristics, bluebeard compound semantic information, question sentence sentence structure dependence and field concept relation are carried out similarity calculating, and concrete grammar sees before and states tour field question sentence similarity calculating method content, realize that finally the similarity of Chinese question sentence is calculated.
Described answer extracting method extracts the relevant issues of user according to field question sentence similarity calculating method, and field question sentence similarity calculating method has merged domain knowledge notion and relation (tour field term and relation), syntactic structure (sentence structure interdependent to and relation) and semantic many features such as (problem typeses) and carried out sentence similarity and calculate.
The present invention has following advantage and effect: the present invention is by means of ontological thought, based on " knowing net " general knowledge storehouse, adopt the KDML descriptive language, define and described tour field term and relation, expansion tour field term description, make up tour field knowledge base-field and know net, realized the fusion that net " is known " in " tour field is known net " and general knowledge storehouse.By morphology, sentence structure and semantic analysis user natural language problem, extract identification problem keyword, expansion word, problem category, question sentence trunk, the interdependent reciprocity question sentence sign of sentence structure, and in conjunction with domain knowledge, sentence structure dependence, semantic relation, realize the calculating of question sentence similarity, and be calculated as the basis, the relevant question sentence of retrieval from the candidate problem set with similarity, extract problem answers, the Chinese question answering system of tour field frequently asked question (FAQ) can be realized, and have efficient, quick, accurate.Yunnan tourism FAQ question answering system test result shows that this method is feasible, and effect is preferably arranged.
Description of drawings
Fig. 1 is a tourism FAQ question answering system structural drawing.
Embodiment
Embodiment
As Fig. 1, tour field FAQ Chinese question answering system implementation method provided by the invention, concrete steps are as follows:
Step 1, FAQ collects and tissue: the FAQ storehouse is the core resource of question and answer, mainly by three kinds of approach acquisitions: climb automatically from the internet by web crawlers for first kind and get, and enter the FAQ storehouse by artificial screening; Second kind is by artificially collecting and put in order acquisition, at tour field, the special relevant issues such as relevant introduction, admission ticket, traffic such as place, sight spot, local conditions and customs, hotel of collecting, taxonomic revision with organize the FAQ question and answer right; The third then is a non-existent new question sentence by system's automatic recording user input but in the question sentence storehouse, and this class question sentence unification is saved in the question and answer history library, regularly by the manual examination and verification arrangement, the answer and the question sentence of correspondence is put in storage together.
FAQ stores by database mode, for improving access speed, problem (Question) and two relation tables of answer (Answer) have been defined, wherein main storage problem of issue table (Question) and answer index information, comprise question number (QuestionId), problem (Question), problem types (QuestionType) and answer numbering (AnswerId), answer table (AnswerId) storage answer information comprises answer numbering (AnswerId) and answer (Answer).Because relation table only is used for the data storage of FAQ and extract the location of answer, retrieve fast for ease of candidate's problem, all question sentences all are index, set up speech-question sentence inverted index literary composition retaining with the speech behind the participle, the selection of candidate question set is extracted from index file, and final result is then according to the answer numbering of depositing in the issue table (AnswerId) directly location output from answer table.Because people often have new problem to add, therefore, need the new problem of often expansion in FAQ, expansion and replacement problem must judge at first whether the problem of new input has same or similar problem in FAQ, basis for estimation is to calculate the similarity of each question sentence in the target question sentence of user's input and the candidate question set, set a threshold value, if similarity is not less than specified threshold value between two question sentences, think that then this question sentence semantically is being equal to the input question sentence among the FAQ, be two kinds of sayings of same problem, do not need to expand relevant issues.If two question sentence similarities are less than assign thresholds, just represent the problem that do not have the user to ask in the existing FAQ storehouse.For this class situation, system at first records this question sentence in the question and answer history library, and regularly by the area of computer aided manual sorting, adds the new problem in the history library and corresponding answer in the FAQ storehouse and sets up increment index, thereby realize the renewal of FAQ data.
Step 2, domain knowledge base makes up: by ontological thought the notion of tour field resource is accurately described, based on " knowing net ", adopt the conceptual description language KDML rule of " knowing net ", set up special tour field body, form tour field resource ontology library field and know dictyosome system, and realized the fusion that net " is known " in tourism knowledge base " net is known in the field " and general knowledge storehouse.At present altogether tourisms such as the introduction of yunnan tourism sight spot, local conditions and customs, hotel, tourist communications 2012 notions of being correlated with are defined and describe, as: accurately being described below of notion " Shangri-la " and " The Old Town of Lijiang ":
NO.＝130001
The W_C=Shangri-la
G_C＝N
E_C=～be a beautiful place
W_E＝xigelila
G_E＝N
E_E＝～is?a?beautiful?place
The DEF=place| place, the city| city, ProperName| is special, (Diqing| enlightening celebrating state), (Yunnan| Yunnan Province), (China| China)
NO.＝130002
The W_C=The Old Town of Lijiang
G_C＝N
E_C=～very special
W_E＝Old?Town?of?Lijiang
G_E＝N
E_E＝～is?very?special
The DEF=place| place, ProperName| is special, city| city, past| former times, (scene| scenic spot), (lijiang| Lijing), (Yunman| Yunnan)
Step 3, user inquiring: on the internet, realize the question and answer query interface based on the Web mode, the user can be by the relevant information inquiry of travelling of natural language problem mode;
Step 4, case study: case study is that the natural language problem of user input is analyzed, it is interdependent to information such as, problem typeses, specifically to extract keyword, expansion word, the sentence structure of sign problem:
1) key to the issue speech, expansion word extract:
Key to the issue speech and expansion word are the fundamentals of sign problem, also are the bases that candidate question set retrieval and answer are extracted.Therefore, problem is carried out participle and part of speech mark, but for specific area, because field vocabulary may fail to show in general dictionary, therefore Words partition system can not well be discerned field vocabulary, cause a field vocabulary may be cut into a plurality of universal words, for this reason, by the load domains knowledge base, after to question sentence cutting just, carry out a field term cutting again, thereby well realized field term cutting and part-of-speech tagging, after carrying out word segmentation, removed stop words, extract noun, verb, adjective, limited adverbial word constitutes the key to the issue speech, and utilize " knowing net ", and keyword is carried out the synonym expansion, form the keyword expansion speech.
2) the question sentence sentence structure is interdependent to extracting:
Because complete Chinese sentence is by the trunk composition of sentence and is modified into branch and constitutes, and people often just can understand the general meaning of a sentence from the trunk composition, because there are a lot of difficulties in syntactic analysis fully at present, so when carrying out sentence similarity calculating, can embody similarity between the sentence by the right similarity of effectively arranging in pairs or groups between the sentence.So-called effectively collocation is right to being meant full sentence core word and directly existing with ... the collocation that its effective speech forms, and effectively speech is defined as verb, noun and adjective here, and it is to be determined by the part-of-speech tagging behind the participle.Full sentence core word is the root node of dependency tree.Problem Q1 for example: is there which characteristic red-letter day in the Dai nationality? with problem Q2: what red-letter day the ethnic group in Yunnan has?, wherein effectively taking of question sentence 1 is paired into: have-red-letter day, have-the Dai nationality; Effectively taking of question sentence 2 is paired into: have-red-letter day, have-ethnic group.As long as relatively these collocation between similarity degree, the interdependent shared language technology platform LTP interface to employing Harbin Institute of Technology of sentence obtains.
3) tour field question sentence classification
Problem types is the extract key factor of answer strategy of location answer and formulation, such as the problem of asking " sight spot introduction " type, just can not answer with the content of " special favor " problem types, problem types is having important status aspect similar question sentence retrieval and the answer extraction, when question sentence is analyzed, judge whether two problems are similar, whether the problem types that at first must judge two problems is consistent, if it is consistent, just can carry out further similarity and calculate judgement, otherwise two problems can not be similar.In specific area, because professional relative fixed, so question sentence inquiry content-form is limited relatively, therefore can classify to problem at the question and answer business, improve the accuracy rate of similar question sentence retrieval and answer extraction with this, at tour field, frequently asked question is divided into sight spot introduction, position, sight spot, sight spot ticket price, local delicacies, special product introduction, 23 subclass types such as custom red-letter day, and according to the feature rule of various somes problem typeses of the feature extraction of these problem typeses, such as scape
The point location type, its relevant issues form is: where is * * * * sight spot?, where * * * * sight spot is positioned at?, where * * * sight spot waits if being located in, and its type constitution rule is * * * (sight spot)+be located in/be positioned at /+interrogative (where).Extraction has made up the composition rule of variety of issue type, and by these feature identification problem typeses.
Step 5, answer is extracted: the answer extracting method mainly comprises following step:
1) selection of candidate question set and question sentence index
The purpose of setting up candidate question set is to dwindle seek scope, and comparatively complicated process such as follow-up similarity calculating is all carried out in this relative small range of candidate question set, thereby improves the recall precision of system.Because the effect of candidate question set is to concentrate from extensive question sentence to take out a fuzzy correlation fast but less relatively subclass, therefore, can the stable searching system of selective maturation realize the retrieval of candidate's problem, Lucene is as powerful, a cross-platform searching system, obtained widespread use, as the retrieval of Sogou news section, the help of Jive WEB forum, Cocoon, Eclipse part etc.The selection Lucene that increases income is used for candidate question set retrieval, and this module effectiveness of retrieval and accuracy rate can effectively be guaranteed.
Lucene is when setting up index for file, at first need to be converted into document (Document) object that to discern, each document then is made up of one or more field (Field) object, and field includes a title and corresponding value again, as a project in the hash table.In actual applications, generally all corresponding to a segment information relevant with inquiry or result for retrieval, for example, web page title need appear in the Search Results field, so can add it in the document object to as a field.Field both can be indexed, can be not indexed and directly be saved in the document yet, as for unique ID, just need not index, as long as preserve.
Because Chinese is with English different, lack dividing mark between the word, with the speech is that the Lucene that handles unit can't resolve Chinese text, therefore, following 3 crucial processing procedures have been increased, be used to realize index: at first, the input sentence is carried out word segmentation processing, thereby obtain each word in the question sentence to Chinese text.Next filters the garbage in the question sentence, mainly refers to the filtration of stop words, as filter in the sentence " ", " youngster ", " " etc., punctuation mark etc.Set up index according to the speech that obtains at last.When index building, at first create an index construct module, wherein the analyzer of the memory location of index file and index content is specified in its constructed fuction, order reads each bar record of question sentence table in the FAQ storehouse then, and be index content with the question sentence, the answer ID of question sentence ID, question sentence correspondence is that index key is set up a Lucene document object, and the document object that generates is joined in the rope structure module, and so circulation is up to all question sentences are all joined index file.
To the problem that obtains by retrieval, filter according to the target problem problem types again, from the problem set that retrieval is recalled, remove the problem that does not conform to the target problem problem types and form the candidate question set that answer is extracted.
2) tour field question sentence similarity is calculated
It is the basis that similar question sentence is searched among the FAQ that the question sentence similarity is calculated, it simultaneously also is the key that answer is extracted, it directly influences the order of accuarcy that answer is extracted, current existing multiple Chinese sentence similarity computing method are divided into Three Estate: grammer similarity, semantic similarity and pragmatic similarity usually.The pragmatic similarity has suitable difficulty, and effect is undesirable at present.And in general application, the semantic similarity that calculates sentence just can satisfy the demands substantially.At present the research method that the sentence semantic similarity is calculated mainly contains: based on the method for identical vocabulary, based on the method for semantic dictionary, based on the method for dependency tree, and based on the method for editing distance etc.Wherein, clearly limitation is arranged based on the method for identical vocabulary, then powerless for the replacement between the synonym.And the method for use semantic dictionary can well address this problem, but the method for simple use semantic dictionary is not considered the structure of sentence inside and the interaction relationship between the word, and accuracy rate is not high.Utilize between the sentence sentence structure dependence to carry out similarity based on the method for dependency tree and calculate, considered the syntax structural relationship of sentence, but faced the precision problem of complete syntactic analysis, and do not consider that the nearly justice of synonym of vocabulary in the syntactic structure replaces.Edit distance approach is normally used for the quick fuzzy matching field of sentence, but the editing operation underaction of its regulation does not consider that the synonym of word is replaced yet.
Calculate on the existing problems basis in the parsing sentence similarity, in conjunction with tour field question sentence characteristics, take all factors into consideration semantic distance, sentence structure dependence and the field concept semantic relation factor of speech, a kind of question sentence similarity calculating method has been proposed, this method is at first utilized the field question characteristics, carrying out similar problem according to problem category filters, reach " net is known in the field " knowledge base based on " knowing net " then, it is effectively interdependent right to adopt syntactic analysis to extract question sentence, and utilize interdependent to the notion semantic relation, realize that similarity is calculated between the tour field question sentence.The calculating committed step is as follows:
A. the semantic similarity of speech calculates
Speech is the basis of forming sentence, sentence similarity must show by the similarity that sentence comprises between the speech, in actual applications, it is just the same to tend to occur two question sentence meanings, but its expression-form is different, such as, what sight spot does problem Q3: the Shangri-la have? with problem Q4: there are those joyful places in middle pasture? its main cause is because the synonym and the correlationship of speech cause, the speech that occurs in a question sentence exists certain synonym and related term, therefore, when calculating the word similarity, must consider the synonym and the correlationship of speech, and can not only judge according to the superficial feature of speech own, " net is known in the field " that utilizes " knowing net " and expand in the field carries out the calculation of similarity degree of the word-level of question sentence, by question sentence being known the net disambiguation, and utilize the notion that occurs in the question sentence to calculate similarity between the question sentence, semantic distance between the notion is defined as the adopted former bee-line in adopted elite tree of two notion correspondences, computing method are with reference to the phrase semantic similarity calculating method of Liu Qun based on " knowing net ", and computation process is as follows
Wherein, Sim
1(C
1, C
2) be first independent adopted former similarity, the Sim of notion
2(C
1, C
2) be former similarity, the Sim of other independent justice
3(C
1, C
2) for concerning adopted former similarity and Sim
4(C
1, C
2) be the former similarity in Signifier, β i (1≤i≤4) is an adjustable parameter, and: β
1+ β
2+ β
3+ β
4=1, β
1〉=β
2〉=β
3〉=β
4, β
1〉=0.5.
B. the question sentence sentence structure is interdependent calculates similarity
The sentence structure that can extract question sentence according to the problem syntactic analysis is interdependent right, the effective collocation that has obtained two question sentences in extraction to after, must be relatively two question sentences effectively collocation between similarity, effectively arrange in pairs or groups between question sentence to similarity in order to calculate, at first must calculate two effectively collocation between similarity relation between the equivalent, according to knowing net disambiguation annotation results, employing is based on " knowing net " Word similarity method (A step), calculate interdependent similarity respectively to last two corresponding words, calculate verb respectively such as interdependent similarity similarity with " having " " is arranged " " having-the Dai nationality " and " having-ethnic group ", " the Dai nationality " and " ethnic group " between similarity, get two similar mean values then and obtain two effective interdependent right similarities.Calculate similarity between the question sentence according to interdependent right similarity then, for any two question sentence A and B, the question sentence sentence that A comprises is interdependent to being A
1, A
2..., A
m, the question sentence sentence that B comprises is interdependent to being B
1, B
2..., Bn, at first interdependent to being benchmark with among the question sentence A calculated interdependent to A
i(1≤i≤m) and B
j(similarity S (the A between 1≤j≤n)
i, B
j), select S (A successively according to formula (2)
i, B
j) maximum interdependent right, interdependent to be sky in the A sentence picked out an effective interdependent pair set { a of sentence
i, interdependent to being benchmark with the effective sentence of B equally, it is interdependent to B to calculate sentence
j((similarity between 1≤i≤m) is expressed as S (B for 1≤j≤n) and Ai
j, A
i), select S (B successively according to formula (3)
j, A
i) maximum interdependent right, interdependent to be sky in the B sentence picked out an effective interdependent pair set { b of sentence
j.
a
i＝max(S(A
i，B
1)，S(A
i，B
2)，Λ，S(A
i，B
n)) (2)
b
j＝max(S(B
j，A
1)，S(B
j，A
2)，Λ，S(B
j，A
m)) (3)
C. tour field question sentence similarity is calculated
Judgement of question sentence type and question sentence are interdependent to after extracting carrying out, just can be according to interdependent two interdependent pair set { a that obtain that similarity is calculated
i, { b
jCarry out the calculating of question sentence similarity, computing formula is as follows:
Adopt that effective sentence structure is interdependent to be calculated carrying out the question sentence similarity, reduce the complexity of algorithm greatly, accuracy rate can obtain raising to a certain degree.This method is considered from the shallow-layer sentence structure, has considered the dependence between speech and the speech, and is more abundant to the understanding of sentence, thereby can obtain the value of sentence similarity more accurately.But existing syntactic analysis technology is ripe not enough, also all syntactic information features all can't be taken into account, so calculating can produce certain error.
3) retrieval of similar problem and answer are extracted
After determining candidate question set, further from Candidate Set, pick out the question sentence the most similar exactly to the target question sentence.The thought of similar question sentence retrieval is the similarity between each question sentence and the target question sentence in the calculated candidate problem set, get similarity greater than the question sentence of the assign thresholds λ of system as similar question sentence, and according to the answer of this question sentence correspondence numbering (AnswerId), Automatic Extraction goes out relevant answer and returns to the user as the output result from database.According to tour field question sentence characteristics, bluebeard compound semantic information, question sentence sentence structure dependence and field concept relation are carried out similarity calculating, and concrete grammar sees before and states tour field question sentence similarity calculating method content, realize that finally the similarity of Chinese question sentence is calculated.
Consider the calculation of similarity degree error, system can also provide preceding 4 suboptimum records when providing optimum answer, selects for the user, if optimum answer is really not relevant, the user can also further search from these candidate answers.From user's angle, by relevant issues being determined to fast among the less set, the practical value of system has just embodied.
The experiment of yunnan tourism FAQ question answering system
At the yunnan tourism field, made up the tour field knowledge base, expanded 2012 field concepts by " knowing net " and obtained " net is known in the field ", adopt retrieval mode with artificial and the Automatic Extraction mode is collected and organized 23335 question and answer right, extract 188 different question sentence type feature rules, adopted the Web mode to realize yunnan tourism FAQ question answering system.At present, this system has been carried out the test of two aspects: be question and answer test on the one hand towards the question sentence corpus, it is right wherein to have collected altogether in the question sentence corpus about 23335 question and answer of yunnan tourism, stores in FAQ, chooses 600 question sentences arbitrarily and test from the question sentence corpus; Be towards actual user's on-the-spot test on the other hand, organized 10 visitor's random enquire problems.Experimental result is as shown in table 1.
Table 1: yunnan tourism FAQ question and answer prototype system test result
| Category of test | The problem number/ | Correctly reply/ | Wrong responses/ | Do not reply/ | Accurate rate % | Recall rate % |
| 1 | 600 | 555 | 24 | 21 | 92.5 | 96.8 |
| 2 | 300 | 250 | 13 | 47 | 83.3 | 87.5 |
From the actual experiment result, from the actual experiment result, She Ji yunnan tourism FAQ question answering system can be practical by this method by this.
Claims (10)
1. tour field FAQ Chinese question answering system implementation method is characterized in that comprising:
(1) FAQ collects and tissue: combining artificial or semi-automatic mode, extract the tourism question and answer from the internet right, and arrangement enters tourism question and answer storehouse, forms the FAQ storehouse of travelling;
(2) tour field construction of knowledge base: make up and safeguard the tour field structure of knowledge and relation, form the tour field knowledge base;
(3) user inquiring: on the internet, the user carries out the travel information inquiry by natural language problem;
(4) case study: the problem to user input is analyzed, and it is interdependent to information such as, problem typeses to extract keyword, expansion word, the sentence structure of sign problem;
(5) answer is extracted: according to the case study result, from the FAQ of frequently asked question storehouse, put forward retrieval candidate problem, adopt the field question similarity calculating method, calculate customer problem and candidate problem similarity, the problem answers that extracts the similarity maximum is as the candidate answer, and offer the user, return the final user and inquire about answer.
2. tour field FAQ Chinese question answering system implementation method according to claim 1 is characterized in that the user can provide the natural language problem towards text, and system directly returns answer, rather than a large amount of webpages relevant with problem.
3. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (1) FAQ collects with method for organizing and is specially: climb automatically from the internet by web crawlers for first kind and get, and enter the FAQ storehouse by artificial screening; Second kind is by artificially collecting and put in order acquisition, at tour field, the special relevant issues such as relevant introduction, admission ticket, traffic such as place, sight spot, local conditions and customs, hotel of collecting, taxonomic revision with organize the FAQ question and answer to and enter the FAQ storehouse; The third then is a non-existent new question sentence by system's automatic recording user input but in the question sentence storehouse, and this class question sentence unification is saved in the question and answer history library, regularly by the manual examination and verification arrangement, the answer and the question sentence of correspondence is gone into the FAQ storehouse together.
4. tour field FAQ Chinese question answering system implementation method according to claim 3, it is characterized in that, the right storage of the question and answer of FAQ is by setting up two relation tables of problem question and answer answer, and by major key Questionid, Answerid carries out the answer index respectively; The storage of issue table, for the ease of quick retrieval, adopt the inverted index mode to store, set up the inverted index document between speech and the question sentence, the selection of candidate question set is extracted from index file, and final result is then according to the answer answerid that deposits in the issue table directly output from database.
5. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (2) has mainly been utilized ontological thought, carries out the expansion of field term by " knowing net " of language message center Mr. Dong Zhendong of institute of the existing Chinese Academy of Sciences.
6. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (2) is specially: by ontological thought the notion of yunnan tourism field resource is accurately described, based on " knowing net ", adopt the conceptual description language KDML rule of " knowing net ", set up special yunnan tourism domain body, form resource ontology library field, yunnan tourism field and know dictyosome system, and realized the fusion that net " is known " in yunnan tourism knowledge base " net is known in the field " and general knowledge storehouse.Altogether relevant 2012 notions of tourisms such as the introduction of yunnan tourism sight spot, local conditions and customs, tourist communications are defined and describe at present, formed the yunnan tourism field and " known net ".
7. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (3) is specially: user inquiring provides user and natural language mode to put question to, and the user can be by be correlated with tour field problem inquiry of internet.
8. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (4) is specially: by the question sentence analysis module problem of user's input is analyzed, mainly comprise lexical analysis, syntactic analysis and semantic analysis, lexical analysis is mainly carried out participle to problem, filter stop words, extract the inquiry core word and carry out keyword expansion by " knowing net "; Syntactic analysis is mainly to extracting the core stem of question sentence, and by Harbin Institute of Technology's parser, it is interdependent right to extract the question sentence sentence structure; The question sentence semantic information is mainly analyzed in semantic analysis, and according to the tour field characteristics, definition is also extracted tour field problem types characteristic sentence mould rule, and realizes the identification of question sentence type by the rule match mode.
9. tour field FAQ Chinese question answering system implementation method according to claim 1, it is characterized in that, described step (5) answer extracting method is specially: question sentence relation information such as the problem types that obtains according to the problem branch, key to the issue speech, problem expansion word, adopt lucene from the FAQ of frequently asked question storehouse, to retrieve the candidate problem set, by the field question similarity calculating method, the candidate problem answers that extracts the similarity maximum is as answer, and returns to the user.
10. tour field FAQ Chinese question answering system implementation method according to claim 9, it is characterized in that the answer extracting method extracts the relevant issues of user according to field question sentence similarity calculating method, field question sentence similarity calculating method merged domain knowledge notion and relation be tour field term and relation, syntactic structure be sentence structure interdependent to and relation and semanteme be that many features such as problem types are carried out sentence similarity and calculated.
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