CN107609101A - Intelligent interactive method, equipment and storage medium - Google Patents
Intelligent interactive method, equipment and storage medium Download PDFInfo
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- CN107609101A CN107609101A CN201710815146.5A CN201710815146A CN107609101A CN 107609101 A CN107609101 A CN 107609101A CN 201710815146 A CN201710815146 A CN 201710815146A CN 107609101 A CN107609101 A CN 107609101A
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Abstract
This application discloses a kind of intelligent interactive method, equipment and storage medium.Methods described includes：Receive customer problem；Calculate the customer problem and the similarity of the problem that prestores in knowledge base；If there are problems that, similarity exceedes prestoring for threshold value, and answer corresponding with the problem that prestores in the knowledge base is exported according to similarity sequence；And the calculation according to user to similarity after the selection record adjustment of output answer；If exceeding the problem that prestores of threshold value in the absence of similarity, the colloquial style of the customer problem is stated into line statement adjustment, and recalculate similarity；If the similarity still not less than threshold value, semantic parsing is carried out to the customer problem, and the answer related to semantic results is searched from knowledge base or internet.Such scheme, it is possible to increase the accuracy and promptness of intelligent replying, and then the reliability of intelligent interaction is provided.
Description
Technical field
The application is related to data processing field, more particularly to intelligent interactive method, equipment and storage medium.
Background technology
As computer and internet continue to develop, the life of people gradually enters into the intelligent epoch.That is, intelligently set
Standby such as computer, mobile phone, tablet personal computer can realize intelligent interaction with people, be provided conveniently, soon for the various aspects of people's life
Prompt service.
Typically, smart machine needs first to carry out semantic parsing to the information of user's input, then is held according to semantic analysis result
Row associative operation, such as corresponding answer is provided.However, corresponding same problem or operational order, due to the expression way of people
Difference, or even the difference of the tone, the representative meaning also differ.At present, smart machine is still present due to can not be correct
Speech recognition goes out the meaning of the natural language of user's input, leads to not provide corresponding reply in time, or accurately reply.Cause
This, improve the accuracy of intelligent replying and promptness be current intelligent interaction major subjects.
The content of the invention
The application is mainly solving the technical problems that provide intelligent interactive method, equipment and storage medium, it is possible to increase intelligence
The accuracy and promptness that can be replied, and then the reliability of intelligent interaction is provided.
In order to solve the above problems, the application first aspect provides a kind of intelligent interactive method, including：User is received to ask
Topic；Calculate the customer problem and the similarity of the problem that prestores in knowledge base；If exist and the user in the knowledge base
Problem similarity exceedes the problem that prestores of threshold value, then according to similarity sequence export in the knowledge base with the similarity
More than the answer to prestore corresponding to problem of threshold value；And according to user to similarity after the selection record adjustment of output answer
Calculation, the similarity highest of the corresponding problem that prestores of the answer that makes customer problem be selected with user；If in the knowledge base
In the absence of the problem that prestores for exceeding threshold value with the customer problem similarity, then progress is stated to the colloquial style of the customer problem
Sentence adjusts, and recalculates the similarity of the problem that prestores in the customer problem and knowledge base after adjustment；If the similarity
Still not less than threshold value, then semantic parsing is carried out to the customer problem and obtains semantic results, and from the knowledge base or internet
It is upper to search the answer related to the semantic results.
In order to solve the above problems, the application second aspect provides a kind of intelligent interaction device, including interconnection
Memory and processor；The processor is used to perform above-mentioned method.
In order to solve the above problems, the application third aspect provides a kind of non-volatile memory medium, is stored with calculating
Machine program, the computer program is used to be run by processor, to perform above-mentioned method.
In such scheme, intelligent interaction device is being found by calculating the similarity of customer problem and the problem that prestores
Similarity exceed threshold value prestore problem when, the answer of the corresponding problem that prestores of output, and according to user to exporting the selection of answer
The calculation of similarity after record adjustment, so that the customer problem problem that prestores corresponding with the answer that user selects is similar
Highest is spent, therefore can ensure that the calculating of follow-up similarity can match with user's communicative habits, improves the accuracy of intelligent replying, therefore
Improve the reliability of intelligent interaction；When do not find similarity more than threshold value prestore problem when, to the spoken language of customer problem
Change is stated to be adjusted into line statement, and recalculates the similarity of the problem that prestores in the customer problem and knowledge base after adjustment；If
Similarity still not less than threshold value, then carries out semantic parsing to customer problem, and searches from knowledge base or internet and tied with semantic
The related answer of fruit, thereby ensures that the timely and accurate of reply, moreover, being stated by adjusting colloquial style, can improve semantic solution
The accuracy of analysis, and then the accuracy of intelligent replying is improved, therefore also improve the reliability of intelligent interaction.
Brief description of the drawings
Fig. 1 is the flow chart of the embodiment of the application intelligent interactive method one；
Fig. 2 is the partial process view of another embodiment of the application intelligent interactive method；
Fig. 3 is the partial process view of the application intelligent interactive method another embodiment
Fig. 4 is the structural representation of the embodiment of the application intelligent interaction device one；
Fig. 5 is the structural representation of the embodiment of the application non-volatile memory medium one.
Embodiment
With reference to Figure of description, the scheme of the embodiment of the present application is described in detail.
In describing below, in order to illustrate rather than in order to limit, it is proposed that such as particular system structure, interface, technology it
The detail of class, thoroughly to understand the application.
The terms " system " and " network " are often used interchangeably herein.The terms "and/or", only
It is a kind of incidence relation for describing affiliated partner, expression may have three kinds of relations, for example, A and/or B, can be represented：Individually
A be present, while A and B be present, these three situations of individualism B.In addition, character "/" herein, typicallys represent forward-backward correlation pair
A kind of as if relation of "or".
Referring to Fig. 1, Fig. 1 is the flow chart of the embodiment of the application intelligent interactive method one.This method is by with processing energy
The intelligent interaction device of power performs, such as the terminal such as computer, mobile phone or server etc..In the present embodiment, this method include with
Lower step：
S110：Receive customer problem.
Intelligent interaction device can obtain the information that user inputs by internet, such as the intelligent interaction device is service
Device, its information inputted by internet acquisition user by user terminal.Or intelligent interaction device is directly inputted by it
Device obtains the information of user's input.
And specifically, intelligent interaction device can receive the voice messaging and text message of user's input.Also, it can connect simultaneously
The voice messaging and text message are received, and it is handled simultaneously.Or intelligent interaction device only receives user's input
Text message or voice messaging.When intelligent interaction device receives voice messaging, the voice messaging is first subjected to voice
Identification obtains corresponding text message.
S120：Calculate the customer problem and the similarity of the problem that prestores in knowledge base.
Specifically, intelligent interaction device is provided with knowledge base, and several problems and corresponding of prestoring are stored with the knowledge base
Answer knowledge point.When carrying out similarity computing, intelligent interaction device can use Shingle algorithms to calculate customer problem and knowledge
The Jaccard coefficients of the problem that prestores in storehouse.
The problem that prestores in knowledge base is traveled through, after calculating the similarity of each prestore problem and customer problem, sentence
It is disconnected to exceed the problem that prestores of threshold value with the presence or absence of the similarity, and following step is correspondingly performed according to judged result.Wherein, the threshold
Value can be set to obtain by user or intelligent interaction device according to actual conditions according to set algorithm.
In a particular application, intelligent interaction device can determine that the user asks based on the different keywords of the customer problem
The multidimensional sequencing of similarity of topic and the problem that prestored in the knowledge base, and synthesis often ties up sequencing of similarity, obtains the user and asks
The similarity of topic and the problem that prestored in the knowledge base.For example, text information is segmented, specifically can be according to by user institute
At least one of the position at place, residing business scenario and user language custom segment to text information, and from institute
At least one keyword selected in word segmentation result in customer problem is stated, is combined according to different keywords or keyword,
To prestoring, problem carries out Similarity Measure, to obtain different sequencing of similarity.The problem of prestoring is being obtained into different similarities
Sequence number or Similarity value in sequence are weighted summation, according to the numerical value obtained after weighted sum as the problem that prestores
Similarity between customer problem.
S130：Judge to whether there is the problem that prestores for exceeding threshold value with the customer problem similarity in the knowledge base,
If in the presence of performing S140, otherwise perform S160.
S140：Answer corresponding with the problem that prestores in the knowledge base is exported according to similarity sequence.
In the present embodiment, answer corresponding with the problem that prestores that the similarity of customer problem exceedes threshold value is obtained from knowledge base
Case, and the similarity size order that the answer of the acquisition is calculated according to such as S120 exports, specific output can behave as
The answer of the acquisition is shown in intelligent interaction device.
In another embodiment, the intelligent interaction device can also calculate associating for customer problem and the problem that prestores of knowledge base
Degree, the answer corresponding to problem that prestores that similarity is exceeded to threshold value are exported by the corresponding degree of association.
S150：Calculation according to user to similarity after the selection record adjustment of the output answer, makes user
The similarity highest of the problem problem that prestores corresponding with the answer that user selects.
In order to ensure that the calculating of follow-up similarity can be accustomed to matching according to user, intelligent interaction device has self study energy
Power, after associated answer is exported, and operation of the user to the associated answer is detected, such as some answers click to output is looked into
See, forward or when other can show the operation that user is paid close attention to the answer, determine that the answer is easily selected by a user and remembered
Record, the selection for exporting answer is recorded according to user, and obtain the problem that prestores corresponding with the output answer of the selection, will obtained
The problem of prestoring the problem of being defined as the semantic matches with customer problem, thus can analyze to obtain expression of the user to problem and practise
It is used, and according to the communicative habits, the calculation of follow-up similarity is adjusted, if the calculating side using the similarity after the adjustment
Formula, the customer problem and the similarity highest of the problem that prestores obtained.
S160：The colloquial style of the customer problem is stated into line statement adjustment, and the user recalculated after adjustment asks
The similarity of topic and the problem that prestores in knowledge base.
In the present embodiment, intelligent interaction device first judges whether customer problem includes colloquial style and state, specifically can be by user
Problem is contrasted with the typical problem in knowledge base, to determine if to include colloquial style statement.If being stated including colloquial style,
Spoken correction can be then carried out to the words for belonging to colloquial style statement in customer problem, spoken language correction may include that word order is overturned, deleted
Remove, replace in any one or any combination.For example, the colloquial style statement that the customer problem includes has what word order overturned
Continuous two words, then sequencing can be carried out to continuous two words that the word order overturns, to form a neologisms.In another example should
Customer problem includes spoken modal particle, then deletes the spoken modal particle.
After line statement adjustment is entered, the customer problem after adjusting will be calculated and stated.Specifically, the calculation of the similarity can
Refering to S120 associated description.
S170：If the similarity still not less than threshold value, semantic parsing is carried out to the customer problem and obtains semantic knot
Fruit, and the answer related to the semantic results is searched from the knowledge base or internet.
For example, if the customer problem after adjustment still can not find the problem that prestores that similarity meets threshold value, to the user
Problem carries out semantic parsing and obtains semantic results, and lookup is related to the semantic results from the knowledge base or internet
Answer, and the answer found is exported, can be according to related to semantic results when the answer specifically probably found has multiple
Degree carrys out the Sequential output answer that finds, specifically can Sequential output should on the output device such as display screen of intelligent interaction device
Answer.
In a concrete application, the intelligent interaction device can be used for instant messaging, and the instant messaging includes wechat, QQ, postal
Case, forum etc..Divide field in the instant messaging in advance to its user, and record has the problem of user was to once answering.Should
The lookup answer related to semantic results from internet in S170, including：The customer problem is sent out by instant messaging
Send the user of problem art or once answer the user of the other problemses of the keyword comprising the customer problem, and will
User is asked to answer in limited time；When receiving the reply of instant communication user, pass back through the user that the instant messaging obtains and answer
It is multiple.
In another embodiment, the intelligent terminal is after the answer related to the semantic results is searched, by the user
Problem and the associated answer found are stored in above-mentioned knowledge base, to be used as prestore problem and correlation new in knowledge base
Answer.
In another embodiment, intelligent interaction device can also be inputted to user according to the user emotion situation detected and prompted
Information.Wherein, the user emotion situation determines according to the keyword of user speed or typing speed, input.For example, intelligence
Interactive device prestores word speed, typing speed and keyword corresponding to different moods.Natural language is inputted by detecting user
When speed (word speed and/or typing speed) and user's input text message in keyword determine active user's feelings
Thread, and input the prompt message related to the user emotion, such as active user's mood as anger, then select carrying for some comforts
Show presentation of information user or play pleasant music.Further, intelligent interaction device can also using user emotion situation as
Scene information described in next embodiment, to determine current semantics scene.Moreover, intelligent interaction device can be combined with user's feelings
Thread situation selection operation corresponding with semantic results, for example, the operation determined according to semantic results is inquiry weather forecast, and works as
Preceding user emotion is anger, then the default tone corresponding with the mood of selection plays weather forecast.
In the present embodiment, intelligent interaction device is being found by calculating the similarity of customer problem and the problem that prestores
Similarity exceed threshold value prestore problem when, the answer of the corresponding problem that prestores of output, and according to user to exporting the selection of answer
The calculation of similarity after record adjustment, so that the customer problem problem that prestores corresponding with the answer that user selects is similar
Highest is spent, therefore can ensure that the calculating of follow-up similarity can match with user's communicative habits, improves the accuracy of intelligent replying, therefore
Improve the reliability of intelligent interaction；When do not find similarity more than threshold value prestore problem when, to the spoken language of customer problem
Change is stated to be adjusted into line statement, and recalculates the similarity of the problem that prestores in the customer problem and knowledge base after adjustment；If
Similarity still not less than threshold value, then carries out semantic parsing to customer problem, and searches from knowledge base or internet and tied with semantic
The related answer of fruit, thereby ensures that the timely and accurate of reply, moreover, being stated by adjusting colloquial style, can improve semantic solution
The accuracy of analysis, and then the accuracy of intelligent replying is improved, therefore also improve the reliability of intelligent interaction.
Please referring to Fig. 2, the semantic parsing that carried out to the customer problem in above-mentioned S170 obtains semantic results, including
Following sub-step：
S171：Semantic parsing is carried out to the customer problem, obtains multiple semantic results.
Specifically can be according to by least one of the location of user, residing business scenario and user language custom pair
Customer problem is segmented, and is selected at least one keyword in customer problem from the word segmentation result or selected
At least one keyword, form to obtain multiple languages of customer problem using the semantic annotation of the difference of at least one keyword
Adopted result.
Because the Expression of language of the user of different places is different, therefore there is also difference to the participle of sentence.Different
User, its speech habits are also different, and intelligent interaction device can input information by collecting user's history, and be directed to each user
The participle model of the user is established to the feedback of the semantic results obtained after participle, the participle model records the participle of the user
Mode, and then active user's problem is segmented according to the participle model.For business scenario it is different, its participle may be present
Difference, for example, user's input " who is the rule of planted agent ", if current business scene is game service scene, will belong to current
" who be planted agent " of scene settings noun does not split, obtain participle for " who is planted agent ", " ", " rule "；Such as current business field
Scape is general service question and answer business scenario, then participle for " who ", "Yes", " planted agent ", " ", " rule ".Thus, intelligent interaction is set
It is standby at least one of to be accustomed to according to the location of above-mentioned user, residing business scenario and user language to customer problem
Segmented.Wherein, if multiple being divided in being accustomed to according to the location of user, residing business scenario and user language
During word, the location of user, residing business scenario and user language can be accustomed to set weight, for according to residing for user
Position, residing business scenario and the user language different participles being accustomed to obtaining, select its weight highest to segment.For example,
The participle obtained according to the location of user is " who ", "Yes", " planted agent ", is according to the participle that residing business scenario obtains
" who is planted agent ", then the participle " who is planted agent " that selection obtains according to the high residing business scenario of weight, or, according to user
The participle that location and user language are accustomed to obtaining is " who ", "Yes", " planted agent ", is obtained according to residing business scenario
Participle be " who be planted agent ", then the weight practised due to the location of user and user language and higher than residing business field
Scape, the then participle " who " for being accustomed to obtaining according to the location of user and user language, "Yes", " planted agent ".
Specifically, its participle mode can such as " most probable number method participle ", " maximum matching participle ", " Dictionary match algorithm ".
The Dictionary match algorithm includes at least one of positive matching, reverse matching, bi-directional matching, maximum matching and smallest match.
Further, after participle, instances of ontology can be carried out to several obtained words, to identify pair of several words
As, the information such as attribute, classification.The body is that one kind of the concept is clearly described in detail, and is that one kind of real world is retouched
Method is stated, or perhaps to the Formal Representation of certain conception of species and its mutual relation in specific area.In local instantiation
Afterwards, several words are the attribute that can obtain its body, are prepared for semantic tagger analysis thereafter.
In addition, before being segmented, denoising first can be carried out to the customer problem of acquisition and modular structureization is handled.
S172：Current semantic scene type is determined according to the scene information detected.
Wherein, the scene information includes the application system that uses of user or application program, user in the application system
Or the current operating information of application program, user are believed in the historical operation information of the application system or application program, context
At least one of breath, subscriber identity information and current context information for collecting.The application system or application that user uses
Program is the application system or application program that intelligent interaction device is currently run, such as is running related application of travelling,
Thus it can be identified as the semantic scene type related to travelling.User believes in the current operation of the application system or application program
Breath is, for example, the searching moving equipment in shopping application program, thus can be identified as the semantic scene class related to the sports equipment
Type.Contextual information is the natural language of the history input of user, and current semantics are also would know that by analyzing contextual information
Scene.The subscriber identity information is the occupational information of user, for example, student, gourmet, building engineer, sportsman etc., root
Semantic scene can be defined as automatically according to the identity information of user related to the identity.The current context information collected can wrap
Ambient noise, current location and current time etc. are included, user's local environment can determine that according to the information, and then obtain being defined as phase
The semantic scene of pass, such as ambient noise is analyzed to obtain for mixed and disorderly vehicle sounds, and be currently peak period on and off duty, then may be used
It is the highway in congestion to determine current semantics scene.
In one embodiment, when the customer problem of acquisition includes voice messaging, the above-mentioned scene information detected may be used also
The type of voice messaging including input, the type of the voice messaging include normally speaking type and sing type.Intelligence is handed over
Mutual equipment can determine its type by detecting the intonation of voice messaging, and select the semantic scene matched with the type, if such as
To sing type, it is determined that the related semantic scene of song.
Intelligent interaction device can establish disaggregated model to every kind of scene information, to pre-set the different situations of every kind of scene
Semantic scene type corresponding to lower.After scene information is detected, every kind of scene information is classified using the disaggregated model,
Default semantic scene type, thereby determines that current semantic scene type corresponding to obtaining.
Wherein, intelligent interaction device can set different weights to every kind of scene information, and the S162 includes：To each described
The scene information detected is classified, and obtains default semantic scene type corresponding with each scene information, and according to each
The weight of the scene information detected chooses one as current semantic scene class from obtained default semantic scene type
Type.For example, including more than above two in the scene information detected, intelligent interaction device is according to every kind of scene information to deserved
The default semantic scene type arrived, when obtained default semantic scene type is multiple, the weight of corresponding scene information may be selected
Highest presets semantic scene type as current semantic scene type；Or selection weight highest two or more presets language
Adopted scene type is drawn as semantic scene type undetermined, and by remaining default semantic scene type according to semantic scene similarity
Divide into the semantic scene type undetermined, will finally be divided into all default semantic scene classes of same semantic scene type undetermined
Weight corresponding to type is added total weight as the semantic scene type undetermined, selects total weight highest semantic scene class undetermined
Type is as current semantic scene type.
S173：Obtain determine the semantic scene type characteristic information, from the multiple semantic results selection with
The characteristic information matching degree highest semantic results of the acquisition.
Specifically, the characteristic information of the semantic scene type includes the focus word under the semantic scene type, everyday words, pass
Join at least one in word.For example, the semantic scene type is motion, then intelligent interaction device collects nearest a period of time (such as
One month) the focus word related to motion, everyday words, such as conjunctive word, " women's volleyball's grand prix ", " swimming " on interior network.Its
In, intelligent interaction device can collect from the social platform of setting, such as microblogging, mhkc etc., and being collected from the social platform makes
It is higher than the focus word of setpoint frequency with frequency, and is more than the conjunctive word of setting value with focus word collocation occurrence number, and deposits
Storage is in the local database.
Intelligent interaction device obtains the characteristic information with the S172 semantic scene type associations determined from local data base,
And the semantic and most similar semantic results of this feature information are selected in the multiple semantic results obtained from S171.
The present embodiment, current semantics scene type is determined by the scene information detected, and pass through current semantics scene
The characteristic information of type determines the semantic results of customer problem, with the semantic results according to determination realizes corresponding operating, due to
The scene information detected according to this can accurately determine to obtain current semantics scene type, and utilize current semantics scene type
Characteristic information assist semantic parsing, the accuracy of semantics recognition can be improved, and then improve the reliability of intelligent interaction.
Referring to Fig. 3, Fig. 3 is the partial process view of another embodiment of the application intelligent interactive method.Above-mentioned S110 includes
Following sub-step：
S111：The voice messaging and the first text message of user's input are received, and language is carried out to the voice messaging
Sound identifies to obtain the second text message.
S112：First text message and the second text message group are combined into the 3rd text envelope according to input sequence
Breath, as the customer problem.
The present embodiment uses the order group according to input by the text message that the voice messaging and user of user's input input
Into the mode of a complete sentence.For example, user inputs text message " in the Water Margin ", then phonetic entry " one of the Mount Liang heroes in Water Margin ", Ran Houwen
This input " introduction ", text message " introduction of the one of the Mount Liang heroes in Water Margin in the Water Margin " is obtained by speech recognition and text combination.Thus
By the way of text and phonetic entry are used cooperatively, even if user runs into the word for being difficult to text input, term also may be selected
Sound inputs, conversely similarly, for will not the word of pronunciation can also use text input, greatly facilitate the information of user to input.
Further, the result that intelligent interaction device is obtained by speech recognition, the word of the first text message of text input can be combined
Justice obtains, for example, obtaining two similar text results by speech recognition, can combine the first text message of text input
The meaning of a word, select rational text results.
In another implementation, intelligent interaction device can use semantic information and text message by user's input respectively to be complete
Whole sentence, and the semanteme by contrasting two complete sentences obtains final semantic results.Specifically, intelligent interaction device obtains user
First text message of text input, and a second independent text message is obtained by speech recognition.Intelligent interaction device pair
First text message and the second text message are performed both by subsequent step when performing S170, and corresponding the is obtained by semanteme parsing
Multiple first semantic results of one text message, and multiple second semantic results of corresponding second text message, from described first
Obtained in semantic results and exceed the first semantic results of given threshold or from the multiple the with the second semantic results matching degree
Acquisition and second semantic results of the first semantic results matching degree more than given threshold in two semantic results, the first of the selection
Semantic results or the second semantic results are obtained multiple semantic results.
Referring to Fig. 4, Fig. 4 is the structural representation of the embodiment of the application intelligent interaction device one.In the present embodiment, the intelligence
Can interactive device 40 concretely the terminal such as computer, mobile phone or server, robot etc. arbitrarily have disposal ability equipment.
The intelligent interaction device 40 includes memory 41, processor 42, input unit 43 and output device 44.Wherein, intelligent interaction
Each component of equipment 40 can be coupled by bus, or intelligent interaction device 40 processor 42 respectively with other groups
Part connects one by one.
Input unit 43 is used to produce information in response to user's input operation, or receives the use of other input equipments transmission
The information of family input.For example, the input unit 43 is keyboard, for producing corresponding text to the pressing of keyboard in response to user
Information, the input unit 43 are touch-screen, and corresponding text message is produced for the touching in response to user, the input unit 43
For microphone, corresponding voice messaging is produced for the voice in response to user, the input unit 43 is receiver, and user receives
Text that other equipment is sent, voice messaging etc..
Output device 44 is used to feed back information to user or other equipment user.For example, display screen, player or
Person's transmitter etc..
Memory 41 has knowledge base, and the knowledge base stores problematic and corresponding answer.
Memory 41 be additionally operable to store processor 42 perform computer instruction and processor 42 in processing procedure
Data, wherein, the memory 41 includes non-volatile memory portion, for storing above computer instruction.
Processor 42 controls the operation of the intelligent interaction device 40, and processor 42 can also be referred to as CPU (Central
Processing Unit, CPU).Processor 42 is probably a kind of IC chip, has the processing energy of signal
Power.Processor 42 can also be general processor, digital signal processor (DSP), application specific integrated circuit (ASIC), ready-made compile
Journey gate array (FPGA) either other PLDs, discrete gate or transistor logic, discrete hardware components.It is logical
It can be microprocessor with processor or the processor can also be any conventional processor etc..
In the present embodiment, processor 42 is used for by calling the computer instruction that memory 41 stores：
Receive the customer problem obtained by input unit 43；
Calculate the similarity of the problem that prestores in the knowledge base that the customer problem stores with memory 41；
If there are problems that exceeding prestoring for threshold value with the customer problem similarity in the knowledge base, filled by exporting
Put 44 and exported according to similarity sequence in the knowledge base and exceed the prestoring corresponding to problem of threshold value with the similarity
Answer；And the calculation according to user to similarity after the selection record adjustment of output answer, make customer problem and user
The similarity highest for the problem that prestored corresponding to the answer of selection；
If the problem that prestores for exceeding threshold value with the customer problem similarity is not present in the knowledge base, to the use
The colloquial style of family problem is stated to be adjusted into line statement, and recalculates the problem that prestores in the customer problem and knowledge base after adjustment
Similarity；If the similarity still not less than threshold value, semantic parsing is carried out to the customer problem and obtains semantic results, and
The answer related to the semantic results is searched on the internet from the knowledge base or by output device 44.
Alternatively, processor 42 performs the similarity for calculating the problem that prestores in the customer problem and knowledge base,
Including：Different keywords based on the customer problem determine the customer problem and the multidimensional for the problem that prestored in the knowledge base
Sequencing of similarity, and synthesis often ties up sequencing of similarity, obtains the customer problem and the similar of problem that prestored in the knowledge base
Degree.
Alternatively, processor 42 perform it is described the answer related to the semantic results is being searched by output device 44,
Including：The customer problem is sent to the user or once of problem art using instant messaging mode by output device 44
The user of the other problemses of the keyword comprising the customer problem was answered, and requires that user answers in limited time；Using IMU
News mode is received by input unit 43 obtains user's answer, and the user received is returned by output device 44 and is replied.
Alternatively, processor 42 performs parse semantic to customer problem progress and obtains semantic results, including：To described
Customer problem carries out semantic parsing, obtains multiple semantic results；Current semantic field is determined according to the scene information detected
Scape type, wherein, the scene information include the application system that uses of user or application program, user in the application system or
The current operating information of application program, user the historical operation information of the application system or application program, contextual information,
At least one of subscriber identity information and the current context information that collects；Obtain the semantic scene type of determination
Characteristic information, the characteristic information matching degree highest semantic results of selection and the acquisition from the multiple semantic results.
Further, the characteristic information of the semantic scene type include the semantic scene type under focus word, often
It is at least one in word, conjunctive word.
Further, processor 42 performs described to the semantic parsing of customer problem progress, obtains multiple semantic results
Including：According to by the location of user, residing business scenario and user language custom at least one of to the text
Information is segmented, and at least one keyword in the text message is selected from the word segmentation result；Using described
The difference of at least one keyword is semantic to be annotated to form to obtain multiple semantic results of the text message.
Alternatively, processor 42 performs the customer problem obtained by input unit 43 that receives and included：Reception passes through
The voice messaging and the first text message for user's input that input unit 43 obtains, and speech recognition is carried out to the voice messaging
Obtain the second text message；First text message and the second text message group are combined into the 3rd text according to input sequence
Information, as the customer problem.
Alternatively, processor 42 is related to the semantic results in the execution lookup from the knowledge base or internet
Answer after, be additionally operable to：The customer problem and the associated answer found are stored in knowledge base；
Alternatively, processor 42 performs the meter according to user to similarity after the selection record adjustment of output answer
Calculation mode, including：The communicative habits of customer problem are determined to the selection record of output answer according to user；According to the determination
The calculation of similarity after communicative habits adjustment.
In another embodiment, the processor 42 of the intelligent interaction device 40 can be used for the step for performing above-mentioned embodiment party's rule
Suddenly.
Referring to Fig. 5, the application also provides a kind of embodiment of non-volatile memory medium, the non-volatile memory medium
50 are stored with the computer program 51 that processor can be run, and the computer program 51 is used to perform the method in above-described embodiment.
Specifically, the memory 41 that the storage medium specifically can be as shown in Figure 4.
In such scheme, intelligent interaction device is being found by calculating the similarity of customer problem and the problem that prestores
Similarity exceed threshold value prestore problem when, the answer of the corresponding problem that prestores of output, and according to user to exporting the selection of answer
The calculation of similarity after record adjustment, so that the customer problem problem that prestores corresponding with the answer that user selects is similar
Highest is spent, therefore can ensure that the calculating of follow-up similarity can match with user's communicative habits, improves the accuracy of intelligent replying, therefore
Improve the reliability of intelligent interaction；When do not find similarity more than threshold value prestore problem when, to the spoken language of customer problem
Change is stated to be adjusted into line statement, and recalculates the similarity of the problem that prestores in the customer problem and knowledge base after adjustment；If
Similarity still not less than threshold value, then carries out semantic parsing to customer problem, and searches from knowledge base or internet and tied with semantic
The related answer of fruit, thereby ensures that the timely and accurate of reply, moreover, being stated by adjusting colloquial style, can improve semantic solution
The accuracy of analysis, and then the accuracy of intelligent replying is improved, therefore also improve the reliability of intelligent interaction.
In above description, in order to illustrate rather than in order to limit, it is proposed that such as particular system structure, interface, technology it
The detail of class, thoroughly to understand the application.However, it will be clear to one skilled in the art that there is no these specific
The application can also be realized in the other embodiment of details.In other situations, omit to well-known device, circuit with
And the detailed description of method, in case unnecessary details hinders the description of the present application.
Claims (10)
- A kind of 1. intelligent interactive method, it is characterised in that including：Receive customer problem；Calculate the customer problem and the similarity of the problem that prestores in knowledge base；If there are problems that exceeding prestoring for threshold value with the customer problem similarity in the knowledge base, according to similarity height Exceed the answer to prestore corresponding to problem of threshold value in knowledge base described in Sequential output with the similarity；And according to user to defeated The selection record for going out answer adjusts the calculation of similarity later, and the answer for making customer problem be selected with user is corresponding to prestore The similarity highest of problem；If the problem that prestores for exceeding threshold value with the customer problem similarity is not present in the knowledge base, the user is asked The colloquial style of topic is stated to be adjusted into line statement, and recalculates customer problem and the phase of the problem that prestores in knowledge base after adjustment Like degree；If the similarity still not less than threshold value, carries out semantic parsing to the customer problem and obtains semantic results, and from institute State the answer that lookup is related to the semantic results on knowledge base or internet.
- 2. method according to claim 1, it is characterised in that described to calculate the customer problem and sent one's regards to pre- in knowledge base The similarity of topic, including：Different keywords based on the customer problem determine the customer problem and the multidimensional for the problem that prestored in the knowledge base Sequencing of similarity, and synthesis often ties up sequencing of similarity, obtains the customer problem and the similar of problem that prestored in the knowledge base Degree.
- 3. method according to claim 1, it is characterised in that the lookup from internet is related to the semantic results Answer, including：The customer problem is sent the user of problem art or once answered by instant messaging and includes the user The user of the other problemses of the keyword of problem, and require that user answers in limited time；The user that the instant messaging obtains is passed back through to reply.
- 4. method according to claim 1, it is characterised in that described that semantic knot is obtained to the semantic parsing of customer problem progress Fruit, including：Semantic parsing is carried out to the customer problem, obtains multiple semantic results；Current semantic scene type is determined according to the scene information detected, wherein, the scene information makes including user Application system or application program, user are in the current operating information of the application system or application program, user described The historical operation information of application system or application program, contextual information, subscriber identity information and the current environment collected At least one of information；The characteristic information of the semantic scene type determined is obtained, selection and the acquisition from the multiple semantic results Characteristic information matching degree highest semantic results.
- 5. method according to claim 4, it is characterised in that the characteristic information of the semantic scene type includes the semanteme It is at least one in focus word, everyday words, conjunctive word under scene type.
- 6. according to the method for claim 4, it is characterised in that it is described that semantic parsing is carried out to the customer problem, obtain Multiple semantic results include：The user is asked according to by least one of the location of user, residing business scenario and user language custom Topic is segmented, and at least one keyword in the customer problem is selected from the word segmentation result；Form to obtain multiple semantic results of the customer problem using the semantic annotation of the difference of at least one keyword.
- 7. according to the method for claim 1, it is characterised in that the reception customer problem, including：The voice messaging and the first text message of user's input are received, and speech recognition is carried out to the voice messaging and obtained Second text message；First text message and the second text message group are combined into the 3rd text message according to input sequence, as described Customer problem.
- 8. method according to claim 1, it is characterised in that it is described from the knowledge base or internet search with it is described After the related answer of semantic results, in addition to：The customer problem and the associated answer found are stored in knowledge base；The calculation according to user to similarity after the selection record adjustment of output answer, including：The communicative habits of customer problem are determined to the selection record of output answer according to user；According to the communicative habits of the determination The calculation of similarity after adjustment.
- 9. a kind of intelligent interaction device, it is characterised in that memory and processor including interconnection；The processor is used for the method described in perform claim 1 to 8 any one of requirement.
- 10. a kind of non-volatile memory medium, it is characterised in that be stored with computer program, the computer program is used for quilt Processor is run, and the method described in 1 to 8 any one is required with perform claim.
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