CN106202476B - A kind of interactive method and device of knowledge based collection of illustrative plates - Google Patents
A kind of interactive method and device of knowledge based collection of illustrative plates Download PDFInfo
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- CN106202476B CN106202476B CN201610565229.9A CN201610565229A CN106202476B CN 106202476 B CN106202476 B CN 106202476B CN 201610565229 A CN201610565229 A CN 201610565229A CN 106202476 B CN106202476 B CN 106202476B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
Abstract
The invention discloses a kind of interactive method and device of knowledge based collection of illustrative plates, the method is comprised the following steps：S1：The sentence that user sends is received, and obtains the sentence classification above of the sentence；S2：Determine the final generic of the sentence；S3：Feature Words in the sentence are extracted by knowledge mapping, and judges whether all Feature Words are relevant；S4：The sentence is engaged in the dialogue matching according to chat conversations storehouse；S5：Regular pattern matching is carried out to the sentence；S6：Classification according to sentence is analyzed and processes and generate reply.The classification of control statement to a certain extent of the invention, and make the knowledge question of general knowledge and the Opening field question and answer of customization same in a flow, it is different from existing automatically request-answering system and relies only on the search for carrying out problem answers in chat storehouse and disaggregated model, and the technology of template matches and knowledge mapping search is added in invention, enables to human-computer dialogue more to enrich.
Description
Technical field
The invention belongs to the data processing of nan-machine interrogation, more particularly to a kind of interactive method of knowledge based collection of illustrative plates
And device.
Background technology
Intelligent Answer System in question-response form, the enquirement knowledge required for accurate locating websites user, by with
Website user is interacted, and the information service of personalization is provided for website user.
Intelligent Answer System is the unordered corpus information that will build up on, and carries out the arrangement of orderly and science, and foundation is based on knowing
The disaggregated model of knowledge；These disaggregated models can instruct the language material for newly increasing to seek advice from and information on services, save human resources, improve
The automaticity of information processing, reduces website operating cost.Based on the basic feelings on government and enterprise accumulated for many years to website
Condition FAQs and its answer, arrange the question and answer storehouse form for specification, to support the intelligent answer of various forms problem.Facilitate
User, improves work efficiency, improves corporate image.
Present intelligent Answer System would generally be processed sentence by means of natural language processing and full-text search technology,
Single mode generally can be than relatively low in some of accuracy rate.Conventional intelligent Answer System can only be directed to some field
Question and answer, for the question answering system of open field, currently there are no a kind of general, and autgmentability relatively good handling process
Occur.
Question answering system is mainly classified including sentence, information retrieval, answer extracting, semantic understanding, the aspect such as knowledge mapping
Technology.
The content of the invention
In order to overcome the deficiencies in the prior art, an object of the present invention is a kind of the man-machine of knowledge based collection of illustrative plates of offer
The method of dialogue, it can be answered the problem of general knowledge and Opening field.
The second object of the present invention is to provide a kind of interactive device of knowledge based collection of illustrative plates, its can to general knowledge and
The problem of Opening field is answered.
An object of the present invention is realized using following technical scheme：
A kind of interactive method of knowledge based collection of illustrative plates, comprises the following steps：
S1：The sentence that user sends is received, and obtain the sentence above of the sentence, and determine the sentence and above sentence
Classification；
S2：Classification according to the sentence and the above classification of sentence determine the final generic of the sentence；
S3：Feature Words in the sentence are extracted by knowledge mapping, and judges whether all Feature Words are relevant, if
Have, then the association generation according to Feature Words is replied, if it is not, performing step S4；
S4：The sentence is engaged in the dialogue matching according to chat conversations storehouse, if the match is successful, generation is replied, if lost
Lose, then perform step S5；
S5：Regular pattern matching is carried out to the sentence, if the match is successful, generation is corresponding to reply, if matching is lost
Lose, then perform step S6；
S6：Final generic according to the sentence is analyzed and processes and generate reply.
Preferably, step S2 specifically includes following sub-step：
S21：Classification according to the sentence and the above classification of sentence determine the classification grade and language above of the sentence
The classification grade of sentence；
S22：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is, from institute
Predicate sentence classification as the sentence final generic, if it is not, then from sentence above classification as the sentence
Final generic.The implementation of step S2 can be disclosed further.
Preferably, step S4 specifically includes following sub-step：
S41：Full-text search is carried out to the sentence according to chat conversations storehouse；
S42：The phase of candidate's sentence and read statement in chat conversations storehouses is calculated by Jaccard Similarity algorithms
Like degree；
S43：Judge that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects respective statement to be returned
It is multiple, if less than the threshold value, then perform step S5.Its specific implementation that can further disclose step S4.
Preferably, the threshold value is 0.7.Its setting that can further disclose threshold value.
Preferably, step S6 specifically includes following sub-step：
S61：Final generic according to the sentence extracts the attributive character related to the final generic of the sentence；
S62：According to the extraction program of gained attributive character, attribute corresponding with attributive character is extracted；
S63：Generated according to gained attribute and replied.Its implementation that can further disclose step S6.
The second object of the present invention is realized using following technical scheme：
A kind of interactive device of knowledge based collection of illustrative plates, including with lower module：
Receiver module：Sentence for receiving user's transmission, and obtains the sentence above of the sentence, and determine the sentence and
The classification of sentence above；
Sort module：For the classification according to the sentence and above the classification of sentence come determine the sentence it is final belonging to class
Not；
Knowledge mapping module：For extracting the Feature Words in the sentence by knowledge mapping, and judge that all Feature Words are
It is no relevant, if it has, then the association generation according to Feature Words is replied, if it is not, performing chat matching module；
Chat matching module：For being engaged in the dialogue to the sentence matching according to chat conversations storehouse, if the match is successful, give birth to
Into reply, if it fails, then performing canonical matching module；
Canonical matching module：For carrying out regular pattern matching to the sentence, if the match is successful, generation is corresponding to return
It is multiple, if it fails to match, perform analysis and processing module；
Analysis and processing module：Reply is processed and generates for being analyzed according to the final generic of the sentence.
Preferably, sort module specifically includes following submodule：
Level determination module：Classification according to the sentence and the above classification of sentence determine the classification grade of the sentence
The classification grade of sentence above；
First judge module：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is,
The classification of the sentence is then selected as the final generic of the sentence, if it is not, then from the classification conduct of sentence above
The final generic of the sentence.The specific submodule that sort module is included can be further determined that.
Preferably, chat matching module specifically includes following submodule：
Full-text search module：For carrying out full-text search to the sentence according to chat conversations storehouse；
Computing module：For by candidate's sentence and input in Jaccard Similarity algorithms calculating chat conversations storehouse
The similarity of sentence；
Second judge module：For judging that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects phase
Answer sentence to be replied, if less than the threshold value, then perform canonical matching module.It can further disclose chat matching module institute
Comprising specific module.
Preferably, the threshold value is 0.7.Its numerical value that can further disclose threshold value.
Preferably, analysis and processing module specifically includes following submodule：
Attribute extraction module：For extracting the final generic phase with the sentence according to the final generic of the sentence
The attributive character of pass；
Attribute matching module：For the extraction program according to gained attributive character, attribute corresponding with attributive character is extracted；
Reply module：Replied for being generated according to gained attribute.It can further disclose what analysis and processing module was included
Submodule.
Compared to existing technology, the beneficial effects of the present invention are：
The classification of control statement to a certain extent of the invention, and make the Opening field of the knowledge question of general knowledge and customization
Question and answer are same in a flow, are different from existing automatically request-answering system and rely only on the carrying out in chat storehouse and disaggregated model and ask
The search of answer is inscribed, and the technology of template matches and knowledge mapping search is added in invention, enable to human-computer dialogue
More enrich.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the interactive method of knowledge based collection of illustrative plates of the invention；
Fig. 2 is a kind of structured flowchart of the interactive device of knowledge based collection of illustrative plates of the invention；
Fig. 3 is the classification chart of statement classification of the invention.
Specific embodiment
Below, with reference to accompanying drawing and specific embodiment, the present invention is described further：
As shown in figure 1, the invention provides a kind of interactive method of knowledge based collection of illustrative plates, comprising the following steps：
S1：The sentence that user sends is received, and obtain the sentence above of the sentence, and determine the sentence and above sentence
Classification；
S2：Classification according to the sentence and the above classification of sentence determine the final generic of the sentence；Step S2
Specifically include following sub-step：
S21：Classification according to the sentence and the above classification of sentence determine the classification grade and language above of the sentence
The classification grade of sentence；
S22：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is, from institute
Predicate sentence classification as the sentence final generic, if it is not, then from sentence above classification as the sentence
Final generic.This step be in order to determine in subsequent step to the sentence with which kind of classification come proceed treatment,
The determination of classification results is needed by being compared to the current class in context, then just determines, from that classification, to compare
Mode mainly use and the grade of classification compares, it is preferential with high-grade classification, as shown in figure 3,1 grade is the lowest class, 46
Level is highest level, and from 1 grade to 46 grades, grade rises successively, for example, when user says " I will navigate ", this sentence is undoubtedly
It is " navigation " classification, but, navigation needs a destination, so, rear end can generate " which the destination that may I ask you is "
It is that such as user says " Guangzhou " when client responds to inquire the destination of user, when " Guangzhou " this phrase enters classification, it
" what " this classification may be classified as, and if when the phrase that client replys is classified to other classifications, navigated
This purpose cannot just be completed, so needing category preferences to correct.Due to " navigation ", this is sorted in the session of user
In be not closed, so compare, it is necessary to carry out grade with " navigation " when " what " " Guangzhou " be classified into, due to " navigation "
The height of grade ratio " what ", so " Guangzhou " this phrase needs to obey " navigation " this classification, in this way, many wheel dialogues are just able to
Complete.
But also exception, such as it is weather typing to be in going up in short, then under be in short not divide
Class, then treatment of the sentence as weather typing；
S3：Feature Words in the sentence are extracted by knowledge mapping, and judges whether all Feature Words are relevant, if
Have, then the association generation according to Feature Words is replied, if it is not, performing step S4；By the Feature Words that will be drawn into as
The starting point of the search of knowledge mapping, carries out the association search of knowledge, if whole Feature Words are all relevant, generates answer.
For example, including node Guangzhou in knowledge mapping, then there is attribute city flower, property value is common bombax flower, then formed
Triple (Guangzhou, city flower, common bombax flower).When sentence, " what the city flower in Guangzhou is" or " common bombax flower is city flower where”
When search into collection of illustrative plates, due to including Guangzhou, city flower and common bombax flower in knowledge mapping, then by the extraction to word, so
After find corresponding triple, generation replys that " city flower in Guangzhou is common bombax flower.”.
S4：The sentence is engaged in the dialogue matching according to chat conversations storehouse, if the match is successful, generation is replied, if lost
Lose, then perform step S5；Step S4 specifically includes following sub-step：
S41：Full-text search is carried out to the sentence according to chat conversations storehouse；
S42：The phase of candidate's sentence and read statement in chat conversations storehouses is calculated by Jaccard Similarity algorithms
Like degree；
S43：Judge that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects respective statement to be returned
It is multiple, if less than the threshold value, then perform step S5.The threshold value is 0.7.The step is mainly the sentence of matching chat, for example
When the sentence of the daily greeting chat for occurring " hello " etc in dialogue, then daily chatting can be completed by the step
Its function.
S5：Regular pattern matching is carried out to the sentence, if the match is successful, generation is corresponding to reply, if matching is lost
Lose, then perform step S6；The step is mainly used in systemic-function, the treatment of the special clause of sensitive word and part, for example, work as using
When the sentence of " who are you " etc is asked at family, function of this question sentence equivalent to the about in app is matched and systemic-function
Related information, generation is replied.
S6：Final generic according to the sentence is analyzed and processes and generate reply；Step S6 specifically includes following
Sub-step：
S61：Final generic according to the sentence extracts the attributive character related to the final generic of the sentence；
S62：According to the extraction program of gained attributive character, attribute corresponding with attributive character is extracted；
S63：Generated according to gained attribute and replied.The dialogue for mainly having processed foregoing step process not in this step, example
Such as：" Guangzhou is today cold for user input", the classification of this sentence is weather, mainly has four for the attribute that weather is paid close attention to
Individual time, place, detailed classification and degree, three category can be extracted by the defined attribute extraction program done for weather sentence
Property, the time is today, and place is Guangzhou, and degree is cold, then goes inquiry to obtain weather data according to time and city, further according to
Whether the temperature record in weather data is cold to judge today, so as to complete the answer to the sentence.In this step, it will usually
Need to call other modules to inquire about the information of correlation so as to generate corresponding answer.
As shown in Fig. 2 the invention provides a kind of interactive device of knowledge based collection of illustrative plates, including with lower module：
Receiver module：Sentence for receiving user's transmission, and obtains the sentence above of the sentence, and determine the sentence and
The classification of sentence above；
Sort module：For the classification according to the sentence and above the classification of sentence come determine the sentence it is final belonging to class
Not；Sort module specifically includes following submodule：
Level determination module：Classification according to the sentence and the above classification of sentence determine the classification grade of the sentence
The classification grade of sentence above；
First judge module：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is,
The classification of the sentence is then selected as the final generic of the sentence, if it is not, then from the classification conduct of sentence above
The final generic of the sentence.
Knowledge mapping module：For extracting the Feature Words in the sentence by knowledge mapping, and judge that all Feature Words are
It is no relevant, if it has, then the association generation according to Feature Words is replied, if it is not, performing chat matching module；
Chat matching module：For being engaged in the dialogue to the sentence matching according to chat conversations storehouse, if the match is successful, give birth to
Into reply, if it fails, then performing canonical matching module；Chat matching module specifically includes following submodule：
Full-text search module：For carrying out full-text search to the sentence according to chat conversations storehouse；
Computing module：For by candidate's sentence and input in Jaccard Similarity algorithms calculating chat conversations storehouse
The similarity of sentence；
Second judge module：For judging that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects phase
Answer sentence to be replied, if less than the threshold value, then perform canonical matching module.The threshold value is 0.7.
Canonical matching module：For carrying out regular pattern matching to the sentence, if the match is successful, generation is corresponding to return
It is multiple, if it fails to match, perform analysis and processing module；
Analysis and processing module：Reply is processed and generates for being analyzed according to the final generic of the sentence.Analysis
Processing module specifically includes following submodule：
Attribute extraction module：It is special for extracting the attribute related to the sentence classification according to the final generic of the sentence
Levy；
Attribute matching module：According to the extraction program of gained attributive character, attribute corresponding with attributive character is extracted；
Reply module：Generated according to gained attribute and replied.
It will be apparent to those skilled in the art that technical scheme that can be as described above and design, make other various
It is corresponding to change and deformation, and all these change and deformation should all belong to the protection domain of the claims in the present invention
Within.
Claims (6)
1. a kind of interactive method of knowledge based collection of illustrative plates, it is characterised in that comprise the following steps：
S1：The sentence that user sends is received, the sentence above of the sentence is obtained, and determine the sentence and the above classification of sentence；
S2：Classification according to the sentence and the above classification of sentence determine the final generic of the sentence；Step S2 is specific
Including following sub-step：
S21：Classification according to the sentence and the above classification of sentence determine the classification grade and sentence above of the sentence
Classification grade；
S22：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is, from institute's predicate
Sentence classification as the sentence final generic, if it is not, then from sentence above classification as the final of the sentence
Generic；
S3：Feature Words in the sentence are extracted by knowledge mapping, and judges whether all Feature Words are relevant, if it has, then
Association generation according to Feature Words is replied, if it is not, performing step S4；
S4：The sentence is engaged in the dialogue matching according to chat conversations storehouse, if the match is successful, generation is replied, if it fails, then
Perform step S5；
S5：Regular pattern matching is carried out to the sentence, if the match is successful, generation is corresponding to reply, if it fails to match,
Perform step S6；
S6：Final generic according to the sentence is analyzed and processes and generate reply, and step S6 specifically includes following sub-step
Suddenly：
S61：Final generic according to the sentence extracts the attributive character related to the final generic of the sentence；
S62：According to the extraction program of gained attributive character, attribute corresponding with attributive character is extracted；
S63：Generated according to gained attribute and replied.
2. the interactive method of knowledge based collection of illustrative plates as claimed in claim 1, it is characterised in that step S4 is specifically included
Following sub-step：
S41：Full-text search is carried out to the sentence according to chat conversations storehouse；
S42：The similarity of candidate's sentence and read statement in chat conversations storehouse is calculated by Jaccard Similarity algorithms；
S43：Judge that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects respective statement to be replied, such as
Fruit is less than the threshold value, then perform step S5.
3. the interactive method of knowledge based collection of illustrative plates as claimed in claim 2, it is characterised in that the threshold value is 0.7.
4. a kind of interactive device of knowledge based collection of illustrative plates, it is characterised in that including with lower module：
Receiver module：Sentence for receiving user's transmission, and obtains the sentence above of the sentence, and determines the sentence and above
The classification of sentence；
Sort module：Determine the final generic of the sentence for the classification according to the sentence and the above classification of sentence；
Sort module specifically includes following submodule：
Level determination module：Classification according to the sentence and the above classification of sentence determine the classification grade of the sentence and upper
The classification grade of literary sentence；
First judge module：Judge whether the classification grade of the sentence is more than the classification grade of sentence above, if it is, choosing
With the classification of the sentence as the sentence final generic, if it is not, then from sentence above classification as the language
The final generic of sentence；
Knowledge mapping module：For extracting the Feature Words in the sentence by knowledge mapping, and judge whether all Feature Words have
Association, if it has, then the association generation according to Feature Words is replied, if it is not, performing chat matching module；
Chat matching module：For being engaged in the dialogue to the sentence matching according to chat conversations storehouse, if the match is successful, generate back
It is multiple, if it fails, then performing canonical matching module；
Canonical matching module：For carrying out regular pattern matching to the sentence, if the match is successful, generation is corresponding to reply,
If it fails to match, analysis and processing module is performed；
Analysis and processing module：Reply is processed and generated for being analyzed according to the final generic of the sentence, is analyzed and processed
Module specifically includes following submodule：
Attribute extraction module：It is related to the final generic of the sentence for being extracted according to the final generic of the sentence
Attributive character；
Attribute matching module：For the extraction program according to gained attributive character, attribute corresponding with attributive character is extracted；
Reply module：Replied for being generated according to gained attribute.
5. the interactive device of knowledge based collection of illustrative plates as claimed in claim 4, it is characterised in that chat matching module tool
Body includes following submodule：
Full-text search module：For carrying out full-text search to the sentence according to chat conversations storehouse；
Computing module：For by candidate's sentence and read statement in Jaccard Similarity algorithms calculating chat conversations storehouse
Similarity；
Second judge module：For judging that whether gained similarity, more than a threshold value, if greater than the threshold value, then selects corresponding language
Sentence is replied, and if less than the threshold value, then performs canonical matching module.
6. the interactive device of knowledge based collection of illustrative plates as claimed in claim 5, it is characterised in that the threshold value is 0.7.
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| CN104471568A (en) * | 2012-07-02 | 2015-03-25 | 微软公司 | Learning-based processing of natural language questions |
| CN105677783A (en) * | 2015-12-31 | 2016-06-15 | 上海智臻智能网络科技股份有限公司 | Information processing method and device for intelligent question-answering system |
| CN105677795A (en) * | 2015-12-31 | 2016-06-15 | 上海智臻智能网络科技股份有限公司 | Abstract semantics recommendation method, recommendation device and recommendation system |
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