CN108427722A - intelligent interactive method, electronic device and storage medium - Google Patents
intelligent interactive method, electronic device and storage medium Download PDFInfo
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Abstract
The present invention provides a kind of intelligent interactive method, electronic device and storage medium, this method builds the conversation process template of different business flow, and training intention assessment disaggregated model in advance.After receiving sentence input by user, a series of processing are executed to sentence, including pre-processing, identifying statement type and sentiment analysis, identify the mood classification of user, and using the conversation process template that builds in advance and trained intention assessment disaggregated model, from identifying user view in treated sentence.Later, corresponding knowledge base is inquired according to the user view recognized, query result combination user emotion classification is generated and is replied, user is fed back to.Using the present invention, the accuracy rate of intelligent Answer System can not only be improved, user can also be guided to execute flow dialogue according to the input of user, user is actively guided to complete operation flow on line.
Description
Technical field
The present invention relates to a kind of natural language processing technique field more particularly to intelligent interactive method, electronic device and meters
Calculation machine readable storage medium storing program for executing.
Background technology
In recent years, with the continuous expansion in artificial intelligence application field, each field associated companies all sequentially develop oneself
Chat robots.In existing loan application flow, due to involving a series of safety problems such as wealth, personal information, use
Family may need to seek advice from a large amount of problems during loan.Traditional chat robots generate dialogue using deep learning, partially
It is chatted in amusement, chat robots not accurate to semantic understanding while traditional can only be answered according to the enquirement of user,
Exchange guiding user can not actively be initiated and complete loan operation, interactive experience effect is poor.
Invention content
In view of the foregoing, a kind of intelligent interactive method of present invention offer, electronic device and computer readable storage medium,
Main purpose is to improve the accuracy rate of intelligent Answer System, while user can be guided to execute flow according to the input of user
Dialogue actively guides user to complete operation flow on line.
To achieve the above object, the present invention provides a kind of intelligent interactive method, and this method includes：
Construction step：Build the conversation process template of different business flow, and training intention assessment disaggregated model；
Processing step：Sentence input by user is received, a series of processing are executed to sentence, including pre-process, identify sentence
Type and sentiment analysis obtain the mood classification of user；
Identification step：Using the conversation process template and trained intention assessment disaggregated model built in advance, from processing
User view is identified in sentence afterwards；
Feedback step：Corresponding knowledge base is inquired according to the user view recognized, by query result combination user emotion
Classification, which generates, replys, and feeds back to user.
Preferably, the intention assessment disaggregated model is convolutional neural networks (Convolutional Neural
Network, CNN) model, the training step of the intention assessment disaggregated model is as follows：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Utilize skip-gram or continuous bag of words (Continuous Bag-of-Words, CBOW) model
Training user's language material obtains the word vector of low dimensional；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, set the size of window, by convolution, pondization operation by it is described to
Amount expression is converted into feature vector, carries out the extraction of nonlinear characteristic, warp using Hard Tanh functions to described eigenvector
It crosses after the feature extraction of preset times, obtains the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, above-mentioned switch process, extraction step and big are used
The training method of class training step is trained.
Preferably, the identification step includes：Identify user currently whether flow dialogue in, if user is in flow pair
In words, then conversation content is parsed with the corresponding conversation process template of determination, according to the conversation process template guiding pair built in advance
Flow is talked about, dialogue is outer if user is in flow, and advance trained intention assessment disaggregated model is called to identify user view.
Preferably, the identification step further includes：If trigger flow is talked with before user, and in preset time again
Secondary triggering dialogue then judges that user be in flow dialogue, if user's triggering dialogue for the first time or be more than after preset time again
Whether secondary triggering dialogue then identifies user currently in flow dialogue.
Preferably, the identification step further includes：
By the input by sentence intention assessment disaggregated model, the intention assessment score of the sentence is obtained；
When intention assessment score is greater than or equal to first threshold, judges that user view is classified as business consultation class, work as meaning
Figure identification score judges that the field correlation that user view is classified as chatting in class is asked less than first threshold and when being higher than second threshold
Class is inscribed, when intention assessment score is less than or equal to second threshold, judges that user view is classified as chatting the other problems in class
Class.
Preferably, described pre-process includes：Participle, part-of-speech tagging, name Entity recognition (Named Entities
Recognition, NER), refer to disambiguate and similar word extension.
In addition, the present invention also provides a kind of electronic device, which includes：Memory, processor and display, institute
Storage intelligent interaction program, the intelligent interaction program on memory is stated to be executed, it can be achieved that following steps by the processor：
Construction step：Build the conversation process template of different business flow, and training intention assessment disaggregated model；
Processing step：Sentence input by user is received, a series of processing are executed to sentence, including pre-process, identify sentence
Type and sentiment analysis obtain the mood classification of user；
Identification step：Using the conversation process template and trained intention assessment disaggregated model built in advance, from processing
User view is identified in sentence afterwards；
Feedback step：Corresponding knowledge base is inquired according to the user view recognized, by query result combination user emotion
Classification, which generates, replys, and feeds back to user.
Preferably, the intention assessment disaggregated model is CNN models, and the training step of the intention assessment disaggregated model is such as
Under：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Using skip-gram CBOW model training user's language materials, the word vector of low dimensional is obtained；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, set the size of window, by convolution, pondization operation by it is described to
Amount expression is converted into feature vector, carries out the extraction of nonlinear characteristic, warp using Hard Tanh functions to described eigenvector
It crosses after the feature extraction of preset times, obtains the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, above-mentioned switch process, extraction step and big are used
The training method of class training step is trained.
Preferably, the identification step includes：Identify user currently whether flow dialogue in, if user is in flow pair
In words, then conversation content is parsed with the corresponding conversation process template of determination, according to the conversation process template guiding pair built in advance
Flow is talked about, dialogue is outer if user is in flow, and advance trained intention assessment disaggregated model is called to identify user view.
In addition, to achieve the above object, it is described computer-readable the present invention also provides a kind of computer readable storage medium
Storage medium includes intelligent interaction program, when the intelligent interaction program is executed by processor, it can be achieved that intelligent as described above
Arbitrary steps in exchange method.
Intelligent interactive method, electronic device and computer readable storage medium proposed by the present invention, by being inputted to user
Sentence carry out a series of processing, whether identification user in conversation process.If user is in conversation process, according to pre-
The conversation process template guiding user session flow first built.If user is in except conversation process, using training in advance
Model Identification user view.Corresponding knowledge base is inquired according to user view later, query result combination user emotion is anti-
Feed user, to promote the automatization level of user's loan, improves the accuracy of user's sentence comprehension.
Description of the drawings
Fig. 1 is the schematic diagram of electronic device preferred embodiment of the present invention；
Fig. 2 is the program module schematic diagram of intelligent interaction program preferred embodiment in Fig. 1；
Fig. 3 is the functional schematic of Fig. 2 Program modules；
Fig. 4 is the flow chart of intelligent interactive method preferred embodiment of the present invention；
Fig. 5 is that the invention is intended to identify the flow chart of disaggregated model training.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific implementation mode
It should be appreciated that described herein, specific examples are only used to explain the present invention, is not intended to limit the present invention.
As shown in Figure 1, being the schematic diagram of 1 preferred embodiment of electronic device of the present invention.
In the present embodiment, electronic device 1 can be server, smart mobile phone, tablet computer, PC, portable meter
Calculation machine and other electronic equipments with calculation function.
The electronic device 1 includes：Memory 11, processor 12, display 13, network interface 14 and communication bus 15.Its
In, network interface 14 may include optionally standard wireline interface and wireless interface (such as WI-FI interface).Communication bus 15 is used
Connection communication between realizing these components.
Memory 11 includes at least a type of readable storage medium storing program for executing.The readable storage medium storing program for executing of at least one type
It can be the non-volatile memory medium of such as flash memory, hard disk, multimedia card, card-type memory.In some embodiments, described to deposit
Reservoir 11 can be the internal storage unit of the electronic device 1, such as the hard disk of the electronic device 1.In other embodiments
In, the memory 11 can also be to be equipped on the external memory unit of the electronic device 1, such as the electronic device 1
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..
In the present embodiment, the memory 11 can be used for store be installed on the electronic device 1 application software and
Various types of data, such as intelligent interaction program 10, the conversation process template that builds in advance and trained intention assessment disaggregated model.
Processor 12 can be in some embodiments a central processing unit (Central Processing Unit,
CPU), microprocessor or other data processing chips, the program code for being stored in run memory 11 or processing data, example
Such as execute the training of the computer program code, intention assessment disaggregated model of intelligent interaction program 10.
Display 13 is properly termed as display screen or display unit.Display 13 can be that LED is shown in some embodiments
Device, liquid crystal display, touch-control liquid crystal display and Organic Light Emitting Diode (Organic Light-Emitting
Diode, OLED) touch device etc..Display 13 is for showing the information handled in the electronic apparatus 1 and for showing visualization
Working interface, such as the display sentence of user, reply or the sentence for puing question to user.
Fig. 1 is illustrated only with component 11-15 and the electronic device of intelligent interaction program 10 1, it should be understood that
It is not required for implementing all components shown, the implementation that can be substituted is more or less component.
Optionally, which can also include user interface, and user interface may include input unit such as keyboard
(Keyboard), instantaneous speech power such as sound equipment, earphone etc., optionally user interface can also be connect including the wired of standard
Mouth, wireless interface.
As shown in Fig. 2, being the program module schematic diagram of intelligent interaction program preferred embodiment in Fig. 1.
In embodiments, intelligent interactive method provided by the invention, program and device are illustrated by taking loan transaction as an example
Technical concept, other types of business are equally applicable.
The so-called module of the present invention is the series of computation machine program instruction section for referring to complete specific function.
In the present embodiment, intelligent interaction program 10 includes：Preprocessing module 110, statement type identification module 120, feelings
Feel analysis module 130, dialog engine analysis module 140, template engine module 150, intention assessment training module 160, business to consult
Ask module 170, retrieval similarity module 180 and dialogue generation module 190.
Below in conjunction with the function of the functional schematic specification module 110-190 of the program module of Fig. 3：
Preprocessing module 110, for being pre-processed to sentence input by user, the pretreatment includes：Participle, part of speech
Mark, name Entity recognition refer to disambiguation, the extension of similar word.Further, the participle, part-of-speech tagging, name entity are known
Do not refer to that the natural language processing tool trained using deep neural network is segmented, part-of-speech tagging, names Entity recognition.Institute
It refers to that Chinese character sequence is cut into word sequence to state participle.The part-of-speech tagging refers to being differentiated and being marked according to the part of speech of word
Note, part of speech include：Noun, adverbial word, adjective, verb, pronoun etc., for example, NT represents time noun, V represents verb, NN is represented
Oral noun, PU representative calibration symbol, AD represents adverbial word, PN represents pronoun etc..The name Entity recognition refers to identification sentence
Middle name, place name, institution term etc. name entity, and name entity includes 3 major class, such as entity class, time class and numeric class, and
7 groups, such as name, place name, mechanism name, time, date, currency and percentage.The reference disambiguation refers to eliminating personal pronoun
Reference ambiguity, by using in interdependent syntactic analysis (Dependency Parsing, DP) identification linguistic unit between ingredient
Dependence disclose its syntactic structure, come complete refer to disambiguate.The similar sentence extension refers to utilizing Word2vec technologies
Carry out the extension of similar word.
Statement type identification module 120, result and interdependent syntactic analysis for combining participle, part of speech to differentiate identify
The sentence type of sentence input by user.The sentence type includes：Declarative sentence, imperative sentence, exclamative sentence, interrogative sentence, the present invention
In mainly include interrogative sentence and declarative sentence.
Sentiment analysis module 130, for combining sentence type, by treated sentiment analysis of the input by sentence based on dictionary
Mode and the advance trained sentiment classification model based on deep learning, identify the mood classification of user.The mood class
Do not include：Indignation, anxiety, anger, happy, disappointed, surprised, curious etc..The sentiment analysis mode based on dictionary refers to leading to
It crosses the professional person with grammer sensibility and builds sentiment analysis dictionary, according to the sentiment analysis dictionary of structure：Positive affect word
Allusion quotation, negative affect dictionary and neutral sentiment dictionary, are divided into three classifications by the vocabulary for showing emotion in certain sentence, then right
Than positivity, the number of negativity and neutral emotion word in sentence, the mood classification of sentence is assessed.The emotion based on deep learning
Disaggregated model includes but not limited to shot and long term memory network (Long Short-Term Memory, LSTM) model, supporting vector
Machine (Support Vector Machine, SVM) model, random forest (Random Forests, RF) model and simple pattra leaves
A kind of trained model in advance in this (Naive Bayesian Model, NBM) model.The model is by manual identified
Positive and negative and neutral text is trained by modes such as machine learning, and details are not described herein.
Dialog engine analysis module 140, for identification user currently whether flow dialogue in, if user is in flow pair
In words, then template engine module 150 is called to parse conversation content with the corresponding conversation process template of determination, according to what is built in advance
Conversation process template guides conversation process, if user is in except flow dialogue, intention assessment training module 160 is called to identify
User view.
The conversation process template includes but not limited to loan application flow template, auditing flow template, flow of making loans mould
Plate, refund flow template.The flow template is safeguarded by the language of script formula.It will be appreciated that if user
Trigger flow is talked with before, i.e., tentatively identifies user view, and the triggering dialogue again in preset time, then give tacit consent to use
Family is in flow dialogue, and template engine module 150 is called to parse conversation content with the corresponding conversation process template of determination.If with
Triggering talks with or more than dialogue is triggered after preset time again for the first time at family, then dialog engine analysis module 140 is called to judge to use
Whether family is currently in flow dialogue.It is assumed that preset time is 2 hours, if user's last time trigger flow is talked with, more than 2
Whether triggering dialogue again after hour then calls dialog engine analysis module 140 to identify the user currently in flow dialogue.
Template engine module 150 is intended to the conversation process template to match, according to flow mould for searching with active user
The flow of plate setting guides user.For example, user view matches with loan application flow template, then template engine module 150
Guiding user's completion data is filled in, data verification guides if user has loan qualification and puts question to user's gold that needs are provided a loan
Volume.If user does not have loan qualification, user is prompted to improve data.
Intention assessment training module 160, for the training of intention assessment disaggregated model and by user's treated sentence
It inputs in intention assessment disaggregated model, identifies the intention of user.In the present embodiment, user view is divided into two major classes, including：
Loan consulting class and chat class.Further, each big classification is divided into as several small classifications, for carrying out essence to customer problem
Determine position.For example, loan consulting class includes：Application qualification consulting, mortgage consulting, mode of repayment consulting, is examined the consulting of application amount
Input by sentence is intended to know classification mould by treated for the consulting of core duration, repayment schedule consulting ... intention assessment training module 160
After type, an intention assessment score can be exported for each classification, is maximized the intention that corresponding classification is user.Wherein,
When intention assessment score is greater than or equal to first threshold, judges that user view is classified as loan consulting class, call business consultation
Module 170 inquires answer from from domain knowledge base.When intention assessment score is less than first threshold and is higher than second threshold, sentence
Disconnected user view is classified as chatting the field relevant issues class in class, calls retrieval similarity module 180 from field relevant knowledge
Answer is inquired in library.When intention assessment score is less than or equal to second threshold, judge that user view is classified as chatting in class
Other problems class calls retrieval similarity module 180 to inquire answer from chat question answering system.It is assumed that first threshold is 60 points,
Second threshold be 30 points, when intention assessment score be 46 timesharing, judge that user view is classified as field relevant issues class.The meaning
Figure identification disaggregated model is that training obtains CNN models in advance, it is intended that identifies that the training step of disaggregated model is as follows：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Using skip-gram CBOW model training user's language materials, the word vector of low dimensional is obtained；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, set the size of window, by convolution, pondization operation by it is described to
Amount expression is converted into feature vector, carries out the extraction of nonlinear characteristic, warp using Hard Tanh functions to described eigenvector
It crosses after the feature extraction of preset times, obtains the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, above-mentioned switch process, extraction step and big are used
The training method of class training step is trained.
Business consultation module 170, for according to the user's intention, answer being inquired from from domain knowledge base.It is described to lead certainly
Domain knowledge base can solve the problems, such as that business scope degree of correlation is high.For example, it is high in loan field degree of correlation to solve user
Problem.Assuming that when user's inquiry is about loan interest rate problem, after the intention for identifying the user, inquiry is obtained from domain knowledge base
The answer of the problem.The answer from domain knowledge base can be safeguarded by contact staff.Belong to when the problem of certain user
When the corresponding intention assessment score of small classification is relatively low each of under loan consulting class and loan consulting class, contact staff can be true
The fixed problem is new problem, and in the answer for increasing the problem from domain knowledge base, so that the semantic understanding of system is got over
Come more accurate.
Similarity module 180 is retrieved, for according to user view, from field relevant knowledge library and chatting question answering system inquiry
Answer.Wherein, when user view is classified as chatting the field relevant issues class in class, from field, relevant knowledge library inquiry is answered
Case.When user view type is to chat the other problems class in class, answer, the chat question and answer are inquired from question answering system is chatted
System is completely irrelevant for solving the problems, such as.
Field relevant knowledge library is built based on crawler technology, is asked questions to solve field correlation, for example,
In order to solve the problems, such as the financial class unexpected winner of some users enquirement, part financial field correlation is crawled from internet using crawler technology
Knowledge architecture field relevant knowledge library.The knowledge base is retrieved by the way of retrieval.If all small classifications of the knowledge base
Corresponding intention assessment score is too low, then the problems in the problem of proposing user and field relevant knowledge library compare, and adopt
Receive similarity score highest in the relevant knowledge library of field the problem of corresponding answer reply user.It will be appreciated that field phase
It closes the problems in knowledge base and its corresponding answer number is more, therefore, using trained in advance in the relevant knowledge library of field
Local sensitivity accidental projection forest (Locality-Sensitive Hashing Forest, LSH Forest) model is to all
Data divided, will search for every time and be reduced to an acceptable range with the number of point calculated, then establish multiple
LSH Forest, using the synthesis result of forest as final result.
The question answering system of chatting refers to will chat to talk with to be organized into the forms of question and answer pair and build.Further, when with
The problem of family is putd question to and similarity the problem of chatting structure in question answering system are relatively low, phase can be crawled from internet using reptile
The answer of pass is replied, and the form of the answer and problem formation question and answer pair is added in chat system.It should be understood that
Chat question answering system equally using LSH Forest models to all question and answer to dividing.
Talk with generation module 190, is replied for being generated according to the mood classification, user view and query result of user.It answers
Understand, the reply can be arranged different emotions in identical intention, for the different mood of user and reply.Example
Such as, when detecting that the mood of user is anxiety, the emotion and the tone of pacifying property can be added in reply.
As shown in figure 4, being the flow chart of intelligent interactive method preferred embodiment of the present invention.
In the present embodiment, when processor 12 executes the computer program of the intelligent interaction program 10 stored in memory 11
Realize that intelligent interactive method includes：Step S10- steps S40：
Step S10 builds the conversation process template and training intention assessment disaggregated model of different business flow in advance.It is described
Conversation process template includes but not limited to loan application flow template, auditing flow template, flow template of making loans, refund flow mould
Plate.The intention assessment disaggregated model is that training obtains CNN models in advance, as shown in figure 5, being that the invention is intended to identify classification
The flow chart of model training, it is intended that identify that the training step of disaggregated model is as follows：
Step S11 arranges training data, including obtains user's language material and mark the intent classifier of user's language material.For example,
200,000 user's sentences are obtained, which is related to all small classifications of user view type, and marks the big classification of all sentences
And the small classification under big classification.The big classification of the user view type is divided into three categories, including：Loan consulting class and chat
Class, wherein chatting class includes again：Field relevant issues class and other problems class.
Step S12 obtains the word vector of low dimensional using skip-gram CBOW model training user's language materials.Example
Such as, sentence is trained using CBOW models, obtains the low dimensional word vector of each word in each sentence.
The word vector of low dimensional is converted into corresponding vector by step S13 by searching for the mode of word vector table.It is described
Word vector table be that advance structure is completed, by different two or more low dimensional word vectors be converted into table it is corresponding to
Amount.
Step S14 sets the size of window in the dimension of eigenmatrix, is operated the vector by convolution, pondization
Expression is converted into feature vector, carries out the extraction of nonlinear characteristic using Hard Tanh functions to described eigenvector, passes through
After the feature extraction of preset times, the final feature of user's language material is obtained.Such as window is set as 128 dimensions, by word vector table
The vector changed into is converted to feature vector, and the extraction that Hard Tanh functions carry out nonlinear characteristic is used in combination to obtain final feature.
Step S15, using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision.Wherein,
The gradient descent algorithm refers to matrix method.After being iterated training, generation can identify that the major class of user view type is other
Model.
Step S16, each small classification other to major class in intention type, uses the training of above-mentioned steps S13, S14, S15
Mode is trained, and generation can identify the other model of the group of user view type.
Step S20, as shown in figure 5, being the program module and its functional schematic of intelligent interactive method of the present invention.It receives and uses
2 read statement of family executes corresponding processing, including pretreatment, statement type identification and sentiment analysis to the sentence, obtains user
2 mood classification.It is described pretreatment refer to the sentence that preprocessing module 110 inputs user 2 segment, part-of-speech tagging, life
Name Entity recognition refers to disambiguation, similar word extension process.It is assumed that the sentence that user 2 inputs is：" one star of loaning bill that I submits
Phase, more long energy be audited.", participle and part-of-speech tagging after result be：" I/PN submissions/V /U loaning bills/NN mono-
Week/NT/U ,/PU also/AD wants/V how long/AD energy/V audits/V gets off/V./ PU ", name Entity recognition go out in the sentence
" week ".Statement type identification module 120 combines participle, the result of part of speech differentiation and DP to identify the language that user 2 inputs
The sentence type of sentence.For example, according in 2 read statement of user " how long " etc. words analysis, identify that the sentence is doubtful
Question sentence.Sentiment analysis module 130 combines sentence type, by treated sentiment analysis mode of the input by sentence based on dictionary and pre-
The first trained sentiment classification model based on deep learning, identifies the mood classification of user 2.For example, in conjunction with sentence type,
By " how long ", to go out user emotion polarity be negativity to the words recognitions such as " week ", while analyzing the main feelings of user 2 at this time
Thread is " indignation " and " anxiety ".
Step S30, using the conversation process template and trained intention assessment disaggregated model built in advance, after processing
The sentence in identify user view.Dialog engine analysis module 140 identifies that user 2 is current whether in conversation process, if with
Family 2 currently in conversation process, then calls template engine module 160 to parse sentence content, determines corresponding conversation process template,
And the flow of user 2 is guided to talk with according to the conversation process template.Wherein, if having triggered conversation process before user 2, and
Preset time is then given tacit consent in the 2 current session flow of user such as triggering dialogue again in 2 hours, calls template engine module
160 parsing sentence contents, determine corresponding conversation process template.If user 2 is for the first time or more than preset time, after 2 hours
Read statement trigger conversation process, then dialog engine analysis module 140 re-recognize user currently whether flow dialogue in.It is false
Class, application amount consulting class, mortgage consulting class, mode of repayment official communication are seeked advice from if loan consulting class includes but not limited to application qualification
It askes class, audit duration consulting class and repayment schedule and seeks advice from class.The loan consulting class is the big classification of user view type, described
Application qualification seeks advice from class, application amount consulting class, mortgage consulting class, mode of repayment consulting class, audit duration consulting class and refund
Plan consulting class is the small classification of user view type.For example, intention assessment training module 150 is by the sentence of above-mentioned user 2
Trained intention assessment disaggregated model is inputted after processing, identifies that user's 2 is intended that audit duration consulting class, it is to be understood that
Loan audit duration.
Step S40 inquires corresponding knowledge base, by query result combination user emotion class according to the user view recognized
It Sheng Cheng not reply, feed back to user 2.For example, according to above-mentioned sentence " one week of loaning bill that I submits, more long energy audit
Get off." inquiry of business consultation module 170 from domain knowledge base 31, obtain loan audit when a length of fortnight, then talk with life
The mood of user 2 " indignation " and " anxiety " is combined to be replied to user 2 at module 190：" distinguished user, you are good！You take it easy,
Audit is terminated after your one week of loan, thanks！”
In another embodiment, if the sentence that user 2 inputs is：" I wants to borrow 20000 yuan.", participle and part-of-speech tagging
Result afterwards is：" I/PN thinks/VV loans/V20000/M members/RN./ PU ", name Entity recognition go out the name of the currency in the sentence
Entity " 20000 yuan ".Statement type identification module 120 combines participle, the result of part of speech differentiation and interdependent syntactic analysis identification
Go out the sentence type of the sentence of the input of user 2.For example, according to the analysis of the words such as " loan " in 2 read statement of user, identification
It is declarative sentence to go out the sentence.Sentiment analysis module 130 combines sentence type, by treated emotion of the input by sentence based on dictionary
Analysis mode and the advance trained sentiment classification model based on deep learning, identify the mood classification of user 2.For example,
In conjunction with sentence type, it is negativity to go out user emotion polarity by words recognitions such as " loans ", while analyzing the main of user 2 at this time
Mood is " anxiety ".Dialog engine analysis module 140 identifies that user 2 is currently in conversation process, calls template engine module
It is loan application flow template, guiding user's completion data that 150 lookups are intended to the conversation process template to match with active user
It fills in, data verification, if user has loan qualification, informs user's amount of the loan Time Of Release, refund date and gold of refunding
Volume.If user does not have loan qualification, user is prompted to improve data.
The intelligent interactive method that above-described embodiment proposes, by handling sentence input by user, anolytic sentence class
Type, the mood of user judge user whether in flow dialogue later, and user is in flow, it is determined that conversation process mould
Plate, the progress of guiding user's loan, if user is not in flow, according to the different intention of user execute loan consulting class and
The relevant operations such as class are chatted, different replies is generated for user in conjunction with user emotion, interactive, raising Automated water is carried out with user
Flat and user experience promotes semantic understanding accuracy.
In addition, the embodiment of the present invention also proposes a kind of computer readable storage medium, the computer readable storage medium
Include intelligent interaction program 10, following operation is realized when the intelligent interaction program 10 is executed by processor：
Construction step：Build the conversation process template of different business flow, and training intention assessment disaggregated model；
Processing step：Sentence input by user is received, a series of processing are executed to sentence, including pre-process, identify sentence
Type and sentiment analysis obtain the mood classification of user；
Identification step：Using the conversation process template and trained intention assessment disaggregated model built in advance, from processing
User view is identified in sentence afterwards；
Feedback step：Corresponding knowledge base is inquired according to the user view recognized, by query result combination user emotion
Classification, which generates, replys, and feeds back to user.
Preferably, the intention assessment disaggregated model is CNN models, and the training step of the intention assessment disaggregated model is such as
Under：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Using skip-gram CBOW model training user's language materials, the word vector of low dimensional is obtained；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, set the size of window, by convolution, pondization operation by it is described to
Amount expression is converted into feature vector, carries out the extraction of nonlinear characteristic, warp using Hard Tanh functions to described eigenvector
It crosses after the feature extraction of preset times, obtains the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, above-mentioned switch process, extraction step and big are used
The training method of class training step is trained.
Preferably, the identification step includes：Identify user currently whether flow dialogue in, if user is in flow pair
In words, then conversation content is parsed with the corresponding conversation process template of determination, according to the conversation process template guiding pair built in advance
Flow is talked about, dialogue is outer if user is in flow, and advance trained intention assessment disaggregated model is called to identify user view.
Preferably, the identification step further includes：If trigger flow is talked with before user, and in preset time again
Secondary triggering dialogue then judges that user be in flow dialogue, if user's triggering dialogue for the first time or be more than after preset time again
Whether secondary triggering dialogue then identifies user currently in flow dialogue.
Preferably, the identification step further includes：
By the input by sentence intention assessment disaggregated model, the intention assessment score of the sentence is obtained；
When intention assessment score is greater than or equal to first threshold, judges that user view is classified as business consultation class, work as meaning
Figure identification score judges that the field correlation that user view is classified as chatting in class is asked less than first threshold and when being higher than second threshold
Class is inscribed, when intention assessment score is less than or equal to second threshold, judges that user view is classified as chatting the other problems in class
Class.
Preferably, described pre-process includes：Participle, NER, refers to disambiguation and the extension of similar word at part-of-speech tagging.
The specific implementation of the specific implementation mode of the computer readable storage medium of the present invention and above-mentioned intelligent interactive method
Mode is roughly the same, and details are not described herein.
The embodiments of the present invention are for illustration only, can not represent the quality of embodiment.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical scheme of the present invention substantially in other words does the prior art
Going out the part of contribution can be expressed in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disc, CD), including some instructions use so that a station terminal equipment (can be mobile phone,
Computer, server or network equipment etc.) execute method described in each embodiment of the present invention.
It these are only the preferred embodiment of the present invention, be not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of intelligent interactive method is applied to electronic device, which is characterized in that the method includes：
Construction step：Build the conversation process template of different business flow, and training intention assessment disaggregated model；
Processing step：Sentence input by user is received, a series of processing are executed to sentence, including pre-process, identify statement type
And sentiment analysis, obtain the mood classification of user；
Identification step：Using the conversation process template and trained intention assessment disaggregated model built in advance, from treated
User view is identified in sentence；
Feedback step：Corresponding knowledge base is inquired according to the user view recognized, by query result combination user emotion classification
It generates and replys, feed back to user.
2. intelligent interactive method according to claim 1, which is characterized in that the intention assessment disaggregated model is convolution god
Through network model, the training step of the intention assessment disaggregated model is as follows：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Using skip-gram or continuous bag of words training user language material, the word vector of low dimensional is obtained；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, the size of window is set, is operated the vector table by convolution, pondization
Show that formula is converted into feature vector, the extraction of nonlinear characteristic is carried out using Hard Tanh functions to described eigenvector, by pre-
If after the feature extraction of number, obtaining the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, instructed using above-mentioned switch process, extraction step and major class
The training method for practicing step is trained.
3. intelligent interactive method according to claim 1, which is characterized in that the identification step includes：Identification user works as
It is preceding whether flow dialogue in, if user be in flow talk in, parse conversation content with the corresponding conversation process mould of determination
Plate guides conversation process according to the conversation process template built in advance, if user is in flow, dialogue is outer, calls training in advance
Good intention assessment disaggregated model identifies user view.
4. intelligent interactive method according to claim 1 or 3, which is characterized in that the identification step further includes：If user
Trigger flow is talked with before, and the triggering dialogue again in preset time, then judges that user is in flow dialogue, if with
Family triggering dialogue for the first time is more than triggering dialogue again after preset time, then identifies whether user currently talks in flow
In.
5. intelligent interactive method according to claim 3, which is characterized in that the identification step further includes：
By the input by sentence intention assessment disaggregated model, the intention assessment score of the sentence is obtained；
When intention assessment score is greater than or equal to first threshold, judge that user view is classified as business consultation class, when intention is known
Other score judges that user view is classified as chatting the field relevant issues in class less than first threshold and when being higher than second threshold
Class judges that user view is classified as chatting the other problems class in class when intention assessment score is less than or equal to second threshold.
6. intelligent interactive method according to claim 1, which is characterized in that the pretreatment includes：Participle, part of speech mark
Note, name Entity recognition refer to disambiguation and the extension of similar word.
7. a kind of electronic device, which is characterized in that the electronic device includes：Memory, processor and display, the storage
Intelligent interaction program is stored on device, the intelligent interaction program is executed by the processor, it can be achieved that following steps：
Construction step：Build the conversation process template of different business flow, and training intention assessment disaggregated model；
Processing step：Sentence input by user is received, a series of processing are executed to sentence, including pre-process, identify statement type
And sentiment analysis, obtain the mood classification of user；
Identification step：Using the conversation process template and trained intention assessment disaggregated model built in advance, from treated
User view is identified in sentence；
Feedback step：Corresponding knowledge base is inquired according to the user view recognized, by query result combination user emotion classification
It generates and replys, feed back to user.
8. electronic device according to claim 7, which is characterized in that the intention assessment disaggregated model is convolutional Neural net
The training step of network model, the intention assessment disaggregated model is as follows：
Arrange step：Training data is arranged, including obtains user's language material and marks the intent classifier of user's language material；
Training step：Using skip-gram or continuous bag of words training user language material, the word vector of low dimensional is obtained；
Switch process：By searching for the mode of word vector table, the word vector of low dimensional is converted into corresponding vector；
Extraction step：In the dimension of eigenmatrix, the size of window is set, is operated the vector table by convolution, pondization
Show that formula is converted into feature vector, the extraction of nonlinear characteristic is carried out using Hard Tanh functions to described eigenvector, by pre-
If after the feature extraction of number, obtaining the final feature of user's language material；
Major class training step：Using gradient descent algorithm iteration, the training that the major class in intent classifier is carried out having supervision；
Group training step：To each group of major class in intent classifier, instructed using above-mentioned switch process, extraction step and major class
The training method for practicing step is trained.
9. electronic device according to claim 7, which is characterized in that the identification step includes：Identifying user is currently
It is no flow dialogue in, if user be in flow talk in, parse conversation content with the corresponding conversation process template of determination, root
Conversation process is guided according to the conversation process template built in advance, dialogue is outer if user is in flow, calls trained in advance
Intention assessment disaggregated model identifies user view.
10. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes intelligent interaction
Program, it can be achieved that intelligently being handed over as described in any one of claim 1 to 6 when the system intelligent interaction program is executed by processor
The step of mutual method.
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