CN106802951A - A kind of topic abstracting method and system for Intelligent dialogue - Google Patents
A kind of topic abstracting method and system for Intelligent dialogue Download PDFInfo
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- CN106802951A CN106802951A CN201710030377.5A CN201710030377A CN106802951A CN 106802951 A CN106802951 A CN 106802951A CN 201710030377 A CN201710030377 A CN 201710030377A CN 106802951 A CN106802951 A CN 106802951A
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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Abstract
The invention discloses a kind of topic abstracting method and system for Intelligent dialogue, it passes through to extract the session log of visitor and customer service, and dialogue label is set to the session log；Count contribution degree of each topic in the session log to the dialogue label；Each topic under each dialogue label in corpus is ranked up according to the contribution degree, and extracts the larger topic of contribution degree, using the problem in the topic as recommending problem, and using corresponding answer in the topic as recommending answer；Current problem label is automatically extracted according to the problem that visitor proposes, and the current problem label is matched with the dialogue label in corpus, the recommendation answer corresponding to the recommendation problem in the larger topic of contribution degree under the dialogue label is provided to visitor；So as to realize that dialogue topic is extracted in automation so that the problem that intelligent customer service answers visitor is more accurate, complete, and visitor's experience is more preferable.
Description
Technical field
It is particularly a kind of to be somebody's turn to do for the topic abstracting method of Intelligent dialogue and its application the present invention relates to communication technical field
The system of method.
Background technology
With internet and the popularization and application of ecommerce, intelligent customer service is also more and more.Intelligent customer service is extensive
The Industry-oriented application grown up on the basis of knowledge processing, it is related to extensive Knowledge Processing Technology, natural language to manage
Solution technology, Knowledge Management Technology, automatically request-answering system, inference technology etc., with industry universal, only enterprise does not provide
Fine granularity Knowledge Management Technology, also for the communication between enterprise and mass users establishes a kind of fast having based on natural language
The technological means of effect；Can also be simultaneously the statistical analysis information needed for enterprise's offer fine-grained management, and can be saved for enterprise
A large amount of human resources and cost.
In the realization of the whole technology of intelligent customer service, relate generally to dialogue language material pretreatment, model construction, it is semantic parse,
The technologies such as intensified learning, because the extensive knowledge and profound scholarship of Chinese, same dialogue topic often has various form of presentations, such as in the presence of same
Adopted word, near synonym, expressed intact, simplified expression, ambiguity etc., this allows for each identical topic in dialogue corpus, past
It is past to there are various expression；That is, one dialogue topic is not only made up of a problem and an answer, but may be by multiple problems
Constituted with multiple answers.How to position and accurately extract wherein ideal question and answer sentence, be related to the correct of Intelligent dialogue
Property and integrality, and user Experience Degree.
The content of the invention
The present invention is to solve the above problems, there is provided a kind of topic abstracting method and system for Intelligent dialogue, is passed through
Session log is carried out into tag processes and quantum chemical method, so as to realize that dialogue topic is extracted in automation so that intelligent customer service is answered
More accurate during the problem of visitor, visitor's experience is more preferable.
To achieve the above object, the technical solution adopted by the present invention is：
A kind of topic abstracting method for Intelligent dialogue, it is comprised the following steps：
10) by extracting the session log of visitor and customer service, dialogue label is set to the session log；
20) contribution degree of each topic in the session log to the dialogue label is counted；
30) each topic under each dialogue label in corpus is ranked up according to the contribution degree, and extracts tribute
The larger topic of degree of offering, using the problem in the topic as recommendation problem, and answers corresponding answer in the topic as recommendation
Case；
40) problem proposed according to visitor automatically extracts current problem label, and by current the problem label and language
Dialogue label in material storehouse is matched, and the recommendation problem institute in the larger topic of contribution degree under the dialogue label is provided to visitor
Corresponding recommendation answer.
Preferably, described step 10) before, structure label model is also carried out in advance, its language material in corpus
Conversation subject all language materials are classified, dialogue label is configured to different types of language material, obtain label model.
Preferably, described step 10) in be configured dialogue label, be the dialogue label in the label model
Selected the session log and set corresponding dialogue label.
Preferably, described step 30) in each topic is ranked up, refer to will be newly-increased session log and to language material
The all topics in dialog history record in storehouse, carry out the sequence of contribution degree under each self-corresponding dialogue label, also, often
Then automatic rearrangement after secondary newly-increased session log.
Corresponding, the present invention also provides a kind of topic extraction system for Intelligent dialogue, and it includes：
Dialogue label setup module, it passes through to extract the session log of visitor and customer service, sets right to the session log
Words label；
Contribution degree computing module, for counting contribution of each topic in the session log to the dialogue label
Degree；
Topic abstraction module, it is arranged each topic under each dialogue label in corpus according to the contribution degree
Sequence, and extract the larger topic of contribution degree, using the problem in the topic as recommending problem, and by corresponding answer in the topic
As recommendation answer；
Intelligent dialogue module, it automatically extracts current problem label according to the problem that visitor proposes, and this is current
Problem label is matched with the dialogue label in corpus, in providing the larger topic of contribution degree under the dialogue label to visitor
Recommendation problem corresponding to recommendation answer.
Preferably, also including model construction module, the conversation subject of its language material in corpus enters to all language materials
Row classification, dialogue label is configured to different types of language material, obtains label model.
Preferably, the label setup module of talking with is that the dialogue label in the label model is remembered to the dialogue
Record is selected and is set corresponding dialogue label.
Preferably, each topic is ranked up in the topic abstraction module, refers to session log that will be newly-increased and right
The all topics in dialog history record in corpus, carry out the sequence of contribution degree under each self-corresponding dialogue label, and
And, then automatic rearrangement after newly-increased session log every time.
The beneficial effects of the invention are as follows：
(1) a kind of topic abstracting method and system for Intelligent dialogue of the invention, it is by each session log
Dialogue label is set, by calculating contribution degree of each topic under the dialogue label to the dialogue label, and according to tribute
Degree of offering size is ranked up to the topic, and tag processes and quantum chemical method are carried out by by session log, and by contribution degree compared with
Problem and answer in big topic is used as recommendation problem and recommendation answer, so that realizing that automation is extracted talks with topic so that
The problem that intelligent customer service answers visitor is more accurate, complete, and visitor's experience is more preferable；
(2) present invention is by building label model, and session log to increasing newly and to the dialog history note in corpus
Record is configured dialogue label, and all topics are carried out the sequence of contribution degree under each self-corresponding dialogue label, also, often
Then automatic rearrangement after secondary newly-increased session log so that label model can circulation continuous update, and enable corpus
Persistently automatically update and perfect, visitor's experience is become better and better.
Brief description of the drawings
Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes a part of the invention, this hair
Bright schematic description and description does not constitute inappropriate limitation of the present invention for explaining the present invention.In the accompanying drawings：
Fig. 1 is the general flow chart of topic abstracting method in Intelligent dialogue of the invention；
Fig. 2 is the general flow chart of Intelligent dialogue system of the invention.
Specific embodiment
In order that the technical problems to be solved by the invention, technical scheme and beneficial effect are clearer, clear, below tie
The present invention will be described in further detail to close drawings and Examples.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
As depicted in figs. 1 and 2, a kind of topic abstracting method for Intelligent dialogue of the invention, it is comprised the following steps：
10) by extracting the session log of visitor and customer service, dialogue label is set to the session log；
20) contribution degree of each topic in the session log to the dialogue label is counted；
30) each topic under each dialogue label in corpus is ranked up according to the contribution degree, and extracts tribute
The larger topic of degree of offering, using the problem in the topic as recommendation problem, and answers corresponding answer in the topic as recommendation
Case；
40) problem proposed according to visitor automatically extracts current problem label, and by current the problem label and language
Dialogue label in material storehouse is matched, and the recommendation problem institute in the larger topic of contribution degree under the dialogue label is provided to visitor
Corresponding recommendation answer.
Traditional intelligent customer service system mainly carries out autonomous learning in the following ways：
1., by Similar Problems, existing problem in storehouse is recommended；
2. it is automatic to merge similarity problem very high.
But, such autonomous learning is still present very big defect for intelligent customer service system：On the one hand, it is necessary to artificial
More new problems or Similar Problems are imported, corpus is unable to rapid growth and updates；On the other hand, the outstanding customer service in part is excellent
Show words art, it is impossible to be found and study in time.
Strengthened by building label model and rolling being circulated to model in the present embodiment, can realize that corpus continues
Automatically update and perfect, visitor's experience is become better and better.Specifically：
Described step 10) before, structure label model is also carried out in advance, the dialogue master of its language material in corpus
Topic is classified to all language materials, and dialogue label is configured to different types of language material, obtains label model.
Described step 10) in be configured dialogue label, be the dialogue label in the label model to described
Session log is selected and is set corresponding dialogue label.
Described step 30) in each topic is ranked up, refer to will be newly-increased session log and in corpus
All topics in dialog history record, carry out the sequence of contribution degree under each self-corresponding dialogue label, also, newly-increased every time
Then automatic rearrangement after session log.
In addition, the present invention also provides a kind of topic extraction system corresponding with foregoing topic abstracting method, it includes：
Model construction module, the conversation subject of its language material in corpus is classified to all language materials, to difference
The language material of type is configured dialogue label, obtains label model；
Dialogue label setup module, it passes through to extract the session log of visitor and customer service, sets right to the session log
Words label；
Contribution degree computing module, for counting contribution of each topic in the session log to the dialogue label
Degree；
Topic abstraction module, it is arranged each topic under each dialogue label in corpus according to the contribution degree
Sequence, and extract the larger topic of contribution degree, using the problem in the topic as recommending problem, and by corresponding answer in the topic
As recommendation answer；
Intelligent dialogue module, it automatically extracts current problem label according to the problem that visitor proposes, and this is current
Problem label is matched with the dialogue label in corpus, in providing the larger topic of contribution degree under the dialogue label to visitor
Recommendation problem corresponding to recommendation answer.
In the present embodiment, the dialogue label setup module is dialogue label in the label model to described right
Words record is selected and is set corresponding dialogue label.Each topic is ranked up in the topic abstraction module, refers to
Session log that will be newly-increased and to all topics in the dialog history record in corpus, under each self-corresponding dialogue label
Carry out the sequence of contribution degree, also, then automatic rearrangement after newly-increased session log every time.
Specifically, topic extraction process of the invention is as follows：
1. model is built
Combing is carried out to corpus first, is that all of language material is configured dialogue mark according to conversation subject and keyword
Sign, the dialogue label is classified generally according to conversation subject, such as including quality, price, logistics, after-sale service etc., from
And form label model.
2. label
When in use, such as one group of complete dialogue, 10 visitors are had and is recorded with the dialogue interaction of intelligent customer service.First with
Label model sticks corresponding dialogue label for this dialogue, and the dialogue label comes from the label for building and being formed during model
(one group of complete dialogue, potentially include multiple labels).
3. contribution degree is calculated
Calculate in the dialogue of this group, talk with the contribution degree of label per what in short (each topic) talked with to this group, and use number
Value is indicated.Because one group of dialogue there may be multiple dialogue labels, meanwhile, in short may be to two and the dialogue of the above
Label generation contribution degree, or many words same can talk with label and produce contribution degree, so for the dialogue of this group has calculated contribution
After degree, under each corresponding label, the corresponding many dialogues that contribution degree is produced to it will be produced, contribution degree is pressed into these dialogues
Value is ranked up.
4. it is automatic to extract topic
When intelligent customer service answer visitor question when, extract first the corresponding problem label of its visitor's problem (for example, according to
Keyword in problem), and matched with the dialogue label in constructed model.After completing tag match, this is talked with
Extracted by the larger topic of the contribution degree of numerical ordering or problem (recommendation problem) under label, and by the topic or problem institute
Corresponding answer (recommendation answer) is used to answer the problem of visitor, so that visitor obtains more accurate and perfect problem and answers
It is multiple.
5. circulating rolling strengthens model
When having new language material and label is produced, model continuous updating, and according to the calculating of contribution degree, continuous updating each
The contribution degree numerical value of the corresponding problem under label and sequence, the problem for persistently improving Intelligent dialogue are replied.
It should be noted that each embodiment in this specification is described by the way of progressive, each embodiment weight
Point explanation is all difference with other embodiment, between each embodiment identical similar part mutually referring to.
For system class embodiment, because it is substantially similar to embodiment of the method, so description is fairly simple, related part ginseng
See the part explanation of embodiment of the method.Also, herein, term " including ", "comprising" or its any other variant
Including for nonexcludability is intended to, so that process, method, article or equipment including a series of key elements not only include
Those key elements, but also other key elements including being not expressly set out, or also include for this process, method, article or
The intrinsic key element of person's equipment.In the absence of more restrictions, the key element limited by sentence "including a ...", not
Also there is other identical element in the process including the key element, method, article or equipment in exclusion.In addition, this area
Those of ordinary skill is appreciated that all or part of step for realizing above-described embodiment can be completed by hardware, it is also possible to logical
The hardware that program is crossed to instruct correlation is completed, and described program can be stored in a kind of computer-readable recording medium, above-mentioned
The storage medium mentioned can be read-only storage, disk or CD etc..
Described above has shown and described the preferred embodiments of the present invention, it should be understood that the present invention is not limited to this paper institutes
The form of disclosure, is not to be taken as the exclusion to other embodiment, and can be used for various other combinations, modification and environment, and energy
Enough in invention contemplated scope herein, it is modified by the technology or knowledge of above-mentioned teaching or association area.And people from this area
The change and change that member is carried out do not depart from the spirit and scope of the present invention, then all should be in the protection of appended claims of the present invention
In the range of.
Claims (8)
1. a kind of topic abstracting method for Intelligent dialogue, it is characterised in that comprise the following steps：
10) by extracting the session log of visitor and customer service, dialogue label is set to the session log；
20) contribution degree of each topic in the session log to the dialogue label is counted；
30) each topic under each dialogue label in corpus is ranked up according to the contribution degree, and extracts contribution degree
Larger topic, using the problem in the topic as recommend problem, and using corresponding answer in the topic as recommend answer；
40) problem proposed according to visitor automatically extracts current problem label, and by current the problem label and corpus
In dialogue label matched, provided corresponding to the recommendation problem in the larger topic of contribution degree under the dialogue label to visitor
Recommendation answer.
2. a kind of topic abstracting method for Intelligent dialogue according to claim 1, it is characterised in that：Described step
10) before, structure label model is also carried out in advance, the conversation subject of its language material in corpus is divided all language materials
Class, dialogue label is configured to different types of language material, obtains label model.
3. a kind of topic abstracting method for Intelligent dialogue according to claim 2, it is characterised in that：Described step
10) dialogue label is configured in, be dialogue label in the label model session log is carried out selection and
Corresponding dialogue label is set.
4. a kind of topic abstracting method for Intelligent dialogue according to claim 1 or 2 or 3, it is characterised in that：It is described
The step of 30) in each topic is ranked up, refer to will be newly-increased session log and to the dialog history record in corpus
In all topics, carry out the sequence of contribution degree under each self-corresponding dialogue label, also, every time after newly-increased session log then
Automatic rearrangement.
5. a kind of topic extraction system for Intelligent dialogue, it is characterised in that including：
Dialogue label setup module, it passes through to extract the session log of visitor and customer service, and dialogue mark is set to the session log
Sign；
Contribution degree computing module, for counting contribution degree of each topic in the session log to the dialogue label；
Topic abstraction module, it is ranked up according to the contribution degree to each topic under each dialogue label in corpus,
And the larger topic of contribution degree is extracted, using the problem in the topic as recommendation problem, and corresponding answer in the topic is made
To recommend answer；
Intelligent dialogue module, it automatically extracts current problem label according to the problem that visitor proposes, and by the current problem
Label is matched with the dialogue label in corpus, and pushing away in the larger topic of contribution degree under the dialogue label is provided to visitor
Recommend the recommendation answer corresponding to problem.
6. a kind of topic extraction system for Intelligent dialogue according to claim 5, it is characterised in that：Also include model
Module is built, the conversation subject of its language material in corpus is classified to all language materials, different types of language material is entered
Row sets dialogue label, obtains label model.
7. a kind of topic extraction system for Intelligent dialogue according to claim 6, it is characterised in that：The dialogue mark
It is that the dialogue label in the label model is selected the session log and set corresponding right to sign setup module
Words label.
8. a kind of topic extraction system for Intelligent dialogue according to claim 5 or 6 or 7, it is characterised in that：It is described
Each topic is ranked up in topic abstraction module, refer to will be newly-increased session log and to the dialog history note in corpus
All topics in record, carry out the sequence of contribution degree under each self-corresponding dialogue label, also, every time after newly-increased session log
Then automatic rearrangement.
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Denomination of invention: A topic extraction method and system for intelligent conversation Effective date of registration: 20221202 Granted publication date: 20190611 Pledgee: Industrial Bank Limited by Share Ltd. Xiamen branch Pledgor: XIAMEN KUAISHANGTONG TECH. Corp.,Ltd. Registration number: Y2022980024751 |