CN105843897B - A kind of intelligent Answer System towards vertical field - Google Patents
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Abstract
A kind of intelligent Answer System towards vertical field, including put question to module (1), preprocessing module (2), participle and lexical normalisation module (3), purification word module (4), synonym expansion module (5), vocabulary extension or removing module (6), sentence similarity computing module (7) and reply output module (8).The present invention calculates the similarity of user's question sentence by building domain body, calculates dependent on participle technique, the building of domain body, body similarity.The invention has the advantages that application field ontology more accurately understands that user puts question to and is intended to by the sentence similarity algorithm, sentence similarity is calculated, the accuracy rate of question answering system is improved.
Description
Technical field
The present invention relates to a kind of intelligent Answer Systems towards vertical field, have to the semantic analysis accuracy rate in vertical field
Significant and effect.
Background technique
It is divided according to the realization technology of question answering system, comprising: be based on the question answering system of frequently asked questions (FAQ), based on letter
It ceases the question answering system retrieved, the question answering system based on Question Classification and is based on resource description framework (Resource
Description Framework) RDF query question answering system.
Question answering system based on frequently asked questions constructs FAQs (FAQ) question and answer pair, depends on user's question sentence in realization
With the similarity calculation of question sentence in FAQ.In the development process of FAQ question answering system, need to identify the intention of user's question sentence, to two
A sentence carries out similarity calculation, to return to query result.The relevant technologies process of existing FAQ question answering system are as follows: to sentence
After being segmented, removing the pretreatment works such as stop words, word standardization, inverted index table is established, with VSM TF-IDF algorithm
Calculate the similarity of the word array of two sentences.
Question answering system based on information retrieval, the information source of this system are usually the document on network, are returned
Answer is directly extracted from document.
Based on the question answering system of customer problem classification, corresponding template usually is constructed to every a kind of problem and is handled, is increased
By force to the understanding of problem, the accuracy rate of system is improved.
Based on RDF(Resource Description Framework resource description framework, one kind is for describing Web money
The markup language in source) core of question answering system of inquiry is natural language question sentence to be converted into the standard query language of RDF, usually
It is W3C given query language SPARQL, class, example or the attribute word in natural language question sentence being mapped as in ontology.
However the prior art has using the similarity calculating method for being based on " Hownet ", still when calculating Words similarity
Enough semantic analyses are lacked for the vertical field of profession.And the prior art does not consider field when calculating sentence similarity
The weight of vocabulary lacks enough semantic analyses for the vocabulary in the vertical field of profession.
Technical term explanation of the present invention:
Domain body: domain body gives the basic terms and relationship for constituting related fields vocabulary, and combines this
A little terms and relationship define the rules of these vocabulary extensions.
Participle technique: participle is exactly the words recognition of sentence and carry out part-of-speech tagging.
Hownet: " Hownet " (HowNet) is the detailed semantic knowledge dictionary of a comparison.With Chinese and english word institute's generation
The concept of table is description object, is substantially interior to disclose the relationship between concept and concept and between attribute possessed by concept
The commonsense knowledge base of appearance.
Inverted index table: a table is established to word, and records the position of problem corresponding to word.Due to not being by recording
It determines attribute value, but determines the position of record by attribute value, thus referred to as inverted index (inverted index).
VSM: the processing to content of text is reduced to vector space by vector space model (Vector Space Model)
In vector operation, semantic similarity of the similarity of two vector operations as two sentences.
TF-IDF: term frequency-inverse document frequency method (term frequency-inverse document frequency),
On the basis of VSM algorithm, the weight of word is determined according to the frequency of word, calculates the similarity of two sentences.
Summary of the invention
Realization is organically combined the present invention is based on FAQ and based on RDF query technology, proposes a kind of new question answering system and processing
Process improves the accuracy rate of intelligent automatically request-answering system to enhance intelligent Answer System semantic analysis ability.
The technical scheme is that the present invention calculates the similarity of user's question sentence by building domain body, depend on
Participle technique, the building of domain body, body similarity calculate.
The invention has the advantages that by the sentence similarity algorithm, application field ontology more accurately understands use
Family, which is putd question to, to be intended to, and is calculated sentence similarity, is improved the accuracy rate of question answering system.
Detailed description of the invention
Fig. 1 is that present system constitutes block diagram；
Fig. 2 is groundwork program flow diagram of the present invention；
Fig. 3 is the schematic diagram of the taxonomic structure embodiment of ontology of the present invention；
Fig. 4 is the structural schematic diagram of a specific Noumenon property of the invention；
Fig. 5 is the flow chart of working procedure one embodiment of the present invention；
Fig. 6 is ontology baby's character classification by age structural schematic diagram of the invention.
Specific embodiment
Referring to Fig. 1, a kind of intelligent Answer System towards vertical field of the present invention is based primarily upon computer system, including
Consisting of part:
(1) module 1 is putd question to: for inputting (proposition) problem to system.It can be inputted using keyboard, voice input is hand-written
(plate) input, is inputted using image collecting device.
(2) preprocessing module 2: including vertical domain body (database), for by the class in ontology, attribute, Instance Name
Title is added in dictionary for word segmentation, and marks corresponding part of speech.
(3) participle and lexical normalisation module 3: for segmenting to question sentence, and carrying out word standardization, and mark is each
Classification marker in the part of speech and ontology of word.
(4) purify word module 4: for carrying out stop words to the set after participle, remove no practical significance modal particle,
Greeting word.
(5) synonym expansion module 5: for arranging the related Chinese thesaurus in vertical field, the meaning of a word is extended.
(6) ontology expansion module 6: for judging the lexical set after participle, if the vocabulary in ontology, to word
Relationship between remittance is analyzed, and is extended or is deleted, and weight of the vocabulary in sentence is arranged；If not the word in ontology
It converges, is calculated according to the similarity of common words.
(7) it sentence similarity computing module: in conjunction with weight of the vocabulary in sentence, calculates candidate in the library FAQ and asks
The sentence similarity of topic and question sentence.
(8) output module is replied: for exporting the answer of problem.
Referring to fig. 2, groundwork process of the invention includes:
(1) it pre-processes: constructing vertical domain body, the class in ontology, attribute, instance name are added to dictionary for word segmentation
In, and mark corresponding part of speech.
(2) word standardization is segmented and carried out to question sentence, marks the contingency table in the part of speech and ontology of each word
Note.
(3) stop words is carried out to the set after participle, removes modal particle, the greeting word of no practical significance.
(4) the related Chinese thesaurus for arranging vertical field, is extended the meaning of a word.
(5) lexical set after participle is judged, if the vocabulary in ontology, the relationship vocabulary is divided
Analysis, is extended or deletes, and weight of the vocabulary in sentence is arranged；If not the vocabulary in ontology, according to common words
Similarity calculated.
(6) weight of the vocabulary in sentence is combined, the sentence similarity of candidate problem and question sentence in the library FAQ is calculated.
(7) the problem of exporting problem answers: sorting from high to low according to similarity, finally choosing similarity highest, which is used as, to be answered
Case.
System and workflow of the invention are described further below with reference to Fig. 3-Fig. 6.
1. being constructed about vertical domain ontology repository:
Classify to the knowledge in vertical field, analyzes the relationship between concept and its attribute, realize the table of domain knowledge
It reaches.
Class, example in domain body, attribute: class and object are similar in class and example and object-oriented, and attribute is retouched
State the relationship between class or example.
In Fig. 4, " place " is used as a class, has " Suzhou " as its example, there is the reality of a Hui Shi golden clothes series
Example " Wyeth Hui Shi _ 2 sections of Promil Gold milk powder 400g ", its place of production is Suzhou." place of production " connects two realities as attribute
Example.
2. the calculating of Words similarity in ontology:
Vocabulary corresponds to class, example or the attribute in ontology.All concepts form digraph, define parent and direct subclass
Distance be 1, class at a distance from the example be 1, attribute is respectively 1 at a distance from its domain and codomain, vocabulary W1, W2 away from
It is cumulative from according to above-mentioned definition.W0 is the nearest public father node of W1 and W2.Then the semantic similarity of two vocabulary uses formula:
+
Such as Fig. 3: with " Thing " for root node, depth 0, " Wyeth Hui Shi's _ 2 sections of Promil Gold milk powder 400g "
Depth is 5, and the depth of " Wyeth Hui Shi _ 3 sections of 400g of golden clothes Progress milk powder " is 5, their nearest public father node " gold
The depth of dress series " is 4, then their similarity is+ =0.80。
Or:
α is an adjustable parameter, indicates the value apart from its public father node when two Lexical Similarities are 0.5.
Such as Fig. 3: α=1.6 are set, with " Thing " for root node, depth 0, " Wyeth Hui Shi _ 2 sections of Promil Gold milk powder
400g " and " Wyeth Hui Shi _ 3 sections of 400g of golden clothes Progress milk powder ", they away from public father node " golden clothes series " recently away from
From being all 1, then their similarity are as follows:
+ =0.62。
Finally, sorting from high to low according to similarity, finally choose corresponding to first (similarity is highest) problem
Answer is exported as the final result asked a question, and by answer output module.
3. the determination of term weighing in question sentence:
Weight shared by different words is different in user's question sentence, for example question sentence " may I ask colored king's paper diaper either with or without day
This is original-pack? ", the term weighing of " flower king's paper diaper " and " Japan is original-pack " higher than " may I ask ", " having ", " not having ", " ".Specifically
Determination method be:
1) it safeguards and deactivates vocabulary, will " " " " " " etc. exclude without semantic word, it is not counted in sentence similarity calculating.
2) occur the subclass situation adjacent with its parent in question sentence and delete parent.Chinese will appear semantic the case where repeating,
Such as " Hui Shi milk powder " in Fig. 3, Hui Shi is the subclass of milk powder, the information of the information covering parent of subclass, and the information that subclass carries
Specific in further detail, in this case, we need to only consider information entrained by subclass.
3) dependence between word is analyzed, if W1, W2 are modified relationship, and is in the body the relationship of Subject-Verb
Then its object is added in vocabulary.
Such as Fig. 4: the Subject, Predicate and Object triple in shown ontology, " Wyeth Hui Shi _ 2 sections of Promil Gold milk powder 400g-production
Ground-Suzhou ", " Wyeth Hui Shi _ 2 sections of Promil Gold milk powder 400g " is subject, and " place of production " is predicate, and " Suzhou " serves as object
Role.
Example 1: question sentence: " where the place of production Wyeth Hui Shi _ 2 sections of 400g of Promil Gold milk powder is? ", Chinese " A's
" Suzhou " is added in vocabulary by B " in the case where A modifies B.
4) concept in domain body be with the higher vocabulary of the system degree of correlation, and with the increase of concept depth, concept
The information of carrying is more detailed, therefore the term weight in domain knowledge is higher than normal words, and the weight of vocabulary is with the depth of vocabulary
It spends and increases.
Weightw0= 1 +α
Wherein α is an adjustable parameter, adjusts the weight of concept, and the value that α is arranged herein is 1, indicates domain body
The weight of middle concept is between 1 and 2.
In Fig. 3, with " Thing " for root node, depth 0, the depth of " Hui Shi " is 3, it may be assumed that=3,=5, word
The weight of " Hui Shi " is WeightHui Shi = 1 + = 1.6。
5) it is calculated according to the number of words length of vocabulary:
Example 2: for the relationship of milk powder and baby's age, effect of the ontology information of building to semantic analysis.
As Fig. 6 constructs corresponding domain body, system can identify " four according to the number of segment of milk powder and the age of suitable baby
Month " it is in " 0-6 months " range, so that the problem of finding in model answer containing " 0-6 months ", aobvious on output module replying
Show that content is as follows:
Such as Fig. 5, user inputs question sentence " why rice flour has rancid taste? ", system participle while to word into
Row standardization, is standardized as " peculiar smell " for " rancid taste "；Stop words is removed, " there will be " " " to remove；Database is looked into fall to arrange rope
Draw the question sentence for containing [reason, rice flour, peculiar smell] in table, and problem is sorted according to the quantity containing keyword, takes first 15 to ask
Sentence is as candidate problem；Using VSM algorithm, successively calculate this 15 candidate problems participle go stop words result with [reason,
Rice flour, peculiar smell] similarity, sequence；The answer that similarity chooses the problem of highest returns.
Claims (2)
1. a kind of intelligent Answer System towards vertical field, which is characterized in that including consisting of part:
(1) module is putd question to: for inputting problem to system；
(2) preprocessing module: including vertical domain body, for the class in ontology, attribute, instance name to be added to participle word
In allusion quotation, and mark corresponding part of speech；
(3) participle and lexical normalisation module: for segmenting to question sentence, point in the part of speech and ontology of each word is marked
Class label；
(4) it purifies word module: for carrying out stop words to the set after participle, removing modal particle, the greeting of no practical significance
Word；
(5) synonym expansion module: for arranging the related Chinese thesaurus in vertical field, the meaning of a word is extended；
(6) ontology expansion module: for judging the lexical set after participle, if the vocabulary in ontology, between vocabulary
Relationship analyzed, be extended or delete, and weight of the vocabulary in sentence is set；If not the vocabulary in ontology,
It is calculated according to the similarity of common words；
(7) sentence similarity computing module: in conjunction with weight of the vocabulary in sentence, calculate in the library FAQ candidate problem with
The sentence similarity of question sentence；
(8) output module is replied: for exporting the answer of problem；
The workflow of the intelligent Answer System towards vertical field includes:
(1) it pre-processes: constructing vertical domain body, the class in ontology, attribute, instance name are added in dictionary for word segmentation, and
Mark corresponding part of speech；
(2) word standardization is segmented and carried out to question sentence, marks the classification marker in the part of speech and ontology of each word；
(3) stop words is carried out to the set after participle, removes modal particle, the greeting word of no practical significance；
(4) the related Chinese thesaurus for arranging vertical field, is extended the meaning of a word；
(5) lexical set after participle is judged, if the vocabulary in ontology, the relationship vocabulary is analyzed, into
Row extension is deleted, and weight of the vocabulary in sentence is arranged；If not the vocabulary in ontology, according to the similar of common words
Degree is calculated；
(6) weight of the vocabulary in sentence is combined, the sentence similarity of candidate problem and question sentence in the library FAQ is calculated；
(7) the problem of exporting problem answers: sorting from high to low according to similarity, finally choosing similarity highest is corresponding to be answered
Answer of the case as problem.
2. the intelligent Answer System according to claim 1 towards vertical field, which is characterized in that the enquirement module
Using keyboard, voice, the input of hand-written or image collecting device；The answer output module is using display, loudspeaker or beats
Print machine.
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