CN106599998B - The method and system answered based on affective characteristics adjustment robot - Google Patents
The method and system answered based on affective characteristics adjustment robot Download PDFInfo
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Abstract
The present invention provides a kind of method and system answered based on affective characteristics adjustment robot, methods are as follows: obtains the current text information and text information in the past of user's input；According to current text information and past text information, the characteristic information of user is extracted, characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information；Robot affective characteristics information is obtained, in conjunction with user feeling characteristic information, information integration is carried out, obtains both sides' affective characteristics value；The corresponding answer of current text information is provided in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information.The present invention is based on the current text information and past text information during human-computer dialogue, extract user feeling characteristic information and robot affective characteristics information, in conjunction with user's meaning of one's words characteristic information, the answer for providing robot has the emotion of robot, human-computer dialogue is more intelligent, further improves user experience.
Description
Technical field
The present invention relates to artificial intelligence field more particularly to interactive system fields.
Background technique
In existing artificial intelligence conversational system, judge that user talks using corpus and template, conversational system
Suitable answer is found usually using the mode of search.But there is no adjusted back using the emotion of user as clue
Strategy is answered, other than considering user emotion, robot should also select the strategy responded based on the mood of itself.In person to person
Natural dialogue on, emotion is a key factor, the mankind can according to expressed by other side emotion and itself existing emotion come
The answer strategy for adjusting oneself, achievees the effect that communication.
Therefore, defect in the prior art is that in artificial intelligence dialog procedure, robot is unable to base to the answer of user
Emotion in the text information of user's input, and the emotion of robot itself is combined to make intelligent response, it can not provide
It accurately answers, keeps user experience low.
Summary of the invention
In view of the above technical problems, the present invention provide it is a kind of based on affective characteristics adjustment robot answer method and be
System extracts user feeling characteristic information and machine based on the current text information and past text information during human-computer dialogue
Human feelings sense characteristic information, in conjunction with user's meaning of one's words characteristic information, the answer for providing robot has the emotion of robot, man-machine
Dialogue is more intelligent, further improves user experience.
In order to solve the above technical problems, present invention provide the technical scheme that
In a first aspect, the present invention provides a kind of method answered based on affective characteristics adjustment robot, comprising:
Step S1 obtains the current text information and past text information of user's input；
Step S2 extracts the characteristic information of the user, institute according to the current text information and past text information
Stating characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information；
Step S3 obtains robot affective characteristics information, in conjunction with the user feeling characteristic information, carries out information integration,
Both sides' affective characteristics value is obtained, the robot affective characteristics information is that the information interacted by the past with the user is computed
It obtains；
Step S4, according to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, provide it is described ought be above
The corresponding answer of this information.
The present invention provides a kind of method answered based on affective characteristics adjustment robot, technical solutions are as follows: first obtain
The current text information of user's input and in the past text information；Then, according to the current text information and in the past text information,
The characteristic information of the user is extracted, the characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information；
Then, robot affective characteristics information is obtained, in conjunction with the user feeling characteristic information, information integration is carried out, obtains both sides' feelings
Feel characteristic value, the robot affective characteristics information is that the information interacted by the past with the user is computed acquisition；Most
Afterwards, it is corresponding to be provided in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information for the current text information
It answers.
The method answered based on affective characteristics adjustment robot of the invention, based on the current text during human-computer dialogue
Information and in the past text information, extract user feeling characteristic information and robot affective characteristics information, special in conjunction with user's meaning of one's words
Reference breath, the answer for providing robot have the emotion of robot, and human-computer dialogue is more intelligent, further improves user
Experience.
Further, the step S2, specifically:
According to the current text information and past text information, the current text information and in the past text information are obtained
In emotional state information, the emotional state information include current emotional states information, user go over main emotional information and
At least one of past secondary emotional information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In text message, the text message includes the current text information and the semantics information in text information, key in the past
At least one of word information, proper noun information and verb information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In topic information, the topic information includes that current staple of conversation information, current secondary topic information, the past staple of conversation are believed
Breath, past secondary topic information like topic information and at present at least one of hot topic information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In sentence pattern, tone information and language performance information, the language performance information include current dominant language behavioural information and time
Want language performance information；
According to the emotional state information, text message, topic information, sentence pattern, tone information and language performance information, mention
The characteristic information of the user is taken out, the characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information.
Further, in the step S4, specifically:
Data bank is obtained, multiple answers are stored in advance in the data bank；
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by machine learning algorithm, in institute
It states and selects one to respond as the corresponding answer of the current text information in data bank.
Further, in the step S4, specifically:
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by the method for logic rules, certainly
It is dynamic to generate the corresponding answer of the current text information:
According to the user semantic characteristic information, emotional characteristics in the semantic feature information are analyzed；
A kind of logical combination is formed in conjunction with the mood of the robot according to emotional characteristics in the semantic feature information；
According to the logical combination, automatically generated from the answer strategy that the method by logic rules pre-establishes described
The corresponding answer of current text information.
Further, in the step S4, specifically:
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by the method for machine learning, certainly
It is dynamic to generate the corresponding answer of the current text information:
According to the user semantic characteristic information, emotional characteristics in the semantic feature information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of the robot according to emotional characteristics in the semantic feature information；
According to the Response Policy, automatically generates or select from the answer strategy that the method by machine learning pre-establishes
It selects and generates the corresponding answer of the current text information.
Second aspect, the present invention provides a kind of systems answered based on affective characteristics adjustment robot, comprising:
Text information obtains module, for obtaining the current text information and past text information of user's input；
Talk with identification module, for extracting the user's according to the current text information and past text information
Characteristic information, the characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information；
Robot emotion module, for obtaining robot affective characteristics information, in conjunction with the user feeling characteristic information, into
Row information integration, obtains both sides' affective characteristics value, the robot affective characteristics information is to interact by the past with the user
Information be computed acquisition；
Talk with response means, for providing according to user's meaning of one's words characteristic information in conjunction with both sides' affective characteristics value
The corresponding answer of the current text information.
A kind of system answered based on affective characteristics adjustment robot provided by the invention, technical solution are as follows: first pass through
Text information obtains module, for obtaining the current text information and past text information of user's input；Then known by dialogue
Other module, for extracting the characteristic information of the user, the spy according to the current text information and past text information
Reference breath includes user feeling characteristic information and user's meaning of one's words characteristic information；Then by robot emotion module, for obtaining
Robot affective characteristics information carries out information integration, obtains both sides' affective characteristics value in conjunction with the user feeling characteristic information,
The robot affective characteristics information is that the information interacted by the past with the user is computed acquisition；Finally by dialogue
Response means, for providing the current text in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
The corresponding answer of information.
The system answered based on affective characteristics adjustment robot of the invention, based on the current text during human-computer dialogue
Information and in the past text information, extract user feeling characteristic information and robot affective characteristics information, special in conjunction with user's meaning of one's words
Reference breath, the answer for providing robot have the emotion of robot, and human-computer dialogue is more intelligent, further improves user
Experience.
Further, the dialogue identification module, is specifically used for:
According to the current text information and past text information, the current text information and in the past text information are obtained
In emotional state information, the emotional state information include current emotional states information, user go over main emotional information and
At least one of past secondary emotional information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In text message, the text message includes the current text information and the semantics information in text information, key in the past
At least one of word information, proper noun information and verb information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In topic information, the topic information includes that current staple of conversation information, current secondary topic information, the past staple of conversation are believed
Breath, past secondary topic information like topic information and at present at least one of hot topic information；
According to the current text information and past text information, the current text information and in the past text information are obtained
In sentence pattern, tone information and language performance information, the language performance information include current dominant language behavioural information and time
Want language performance information；
According to the emotional state information, text message, topic information, sentence pattern, tone information and language performance information, mention
The characteristic information of the user is taken out, the characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information.
Further, the dialogue response means, are specifically used for:
Data bank is obtained, multiple answers are stored in advance in the data bank；
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by machine learning algorithm, in institute
It states and selects one to respond as the corresponding answer of the current text information in data bank.
Further, the dialogue response means, are specifically used for:
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by the method for logic rules, certainly
It is dynamic to generate the corresponding answer of the current text information:
According to the user semantic characteristic information, emotional characteristics in the semantic feature information are analyzed；
A kind of logical combination is formed in conjunction with the mood of the robot according to emotional characteristics in the semantic feature information；
According to the logical combination, automatically generated from the answer strategy that the method by logic rules pre-establishes described
The corresponding answer of current text information.
Further, the dialogue response means, are specifically used for:
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by the method for machine learning, certainly
It is dynamic to generate the corresponding answer of the current text information:
According to the user semantic characteristic information, emotional characteristics in the semantic feature information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of the robot according to emotional characteristics in the semantic feature information；
According to the Response Policy, automatically generates or select from the answer strategy that the method by machine learning pre-establishes
It selects and generates the corresponding answer of the current text information.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described.
Fig. 1 shows a kind of method answered based on affective characteristics adjustment robot provided by first embodiment of the invention
Flow chart；
Fig. 2 shows a kind of systems answered based on affective characteristics adjustment robot provided by second embodiment of the invention
Schematic diagram.
Specific embodiment
It is described in detail below in conjunction with embodiment of the attached drawing to technical solution of the present invention.Following embodiment is only used for
Clearly illustrate technical solution of the present invention, therefore be intended only as example, and cannot be used as a limitation and limit protection of the invention
Range.
Embodiment one
Fig. 1 shows a kind of method answered based on affective characteristics adjustment robot provided by first embodiment of the invention
Flow chart；As shown in Figure 1, the embodiment of the present invention one provides a kind of method answered based on affective characteristics adjustment robot,
Include:
Step S1 obtains the current text information and past text information of user's input；
Step S2 extracts the characteristic information of user, characteristic information packet according to current text information and past text information
Include user feeling characteristic information and user's meaning of one's words characteristic information；
Step S3 obtains robot affective characteristics information, in conjunction with user feeling characteristic information, carries out information integration, obtains
Both sides' affective characteristics value, robot affective characteristics information are that the information interacted by the past with user is computed acquisition；
It is corresponding to provide current text information in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information by step S4
It answers.
The present invention provides a kind of method answered based on affective characteristics adjustment robot, technical solutions are as follows: first obtain
The current text information of user's input and in the past text information；Then, it according to current text information and past text information, extracts
The characteristic information of user out, characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information；Then, machine is obtained
Human feelings sense characteristic information carries out information integration, obtains both sides' affective characteristics value, machine human feelings in conjunction with user feeling characteristic information
Feeling characteristic information is that the information interacted by the past with user is computed acquisition；Finally, according to user's meaning of one's words characteristic information, knot
Both sides' affective characteristics value is closed, the corresponding answer of current text information is provided.
The method answered based on affective characteristics adjustment robot of the invention, based on the current text during human-computer dialogue
Information and in the past text information, extract user feeling characteristic information and robot affective characteristics information, special in conjunction with user's meaning of one's words
Reference breath, the answer for providing robot have the emotion of robot, and human-computer dialogue is more intelligent, further improves user
Experience.
Specifically, step S2, specifically:
According to current text information and past text information, the mood in current text information and in the past text information is obtained
Status information, emotional state information include current emotional states information, user's past main emotional information and past secondary mood
At least one of information；
According to current text information and past text information, the text in current text information and in the past text information is obtained
Message, text message includes current text information and the semantics information in text information, key word information, proper noun are believed in the past
At least one of breath and verb information；
According to current text information and past text information, the topic in current text information and in the past text information is obtained
Information, topic information includes current staple of conversation information, currently secondary topic information, past staple of conversation information, past are secondary
Topic information likes topic information and at present at least one of hot topic information；
According to current text information and past text information, the sentence in current text information and in the past text information is obtained
Type, tone information and language performance information, language performance information include current dominant language behavioural information and secondary language performance
Information；
According to emotional state information, text message, topic information, sentence pattern, tone information and language performance information, extract
The characteristic information of user, characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information.
Current text information combination contextual information can also be rewritten the information of the rewriting sentence obtained after original sentence
Be added to extract characteristic information source information in, with emotional state information, text message, topic information, sentence pattern, tone information and
Language performance information extracts the characteristic information of user together.
Wherein, the information for rewriting sentence is phenomenon in order to solve some sentence information deficiencies.
Such as:
Do A: you like watching movie?
B: like.
But B merely enters " liking ", information content is insufficient at this time." like seeing if just will become according to rewriting above
Film ", computer can make more accurate judgement at this time.
Original sentence: retain the appearance of original sentence, computer capacity is allowed to access original sentence.Sentence is rewritten in cooperation, more
It is fully understood by user's input.
Such as:
A: we will go to the cinema tomorrow
B: En En.
At this point, " En En " can be rewritten into " I will go to the cinema tomorrow ".If retaining former sentence " En En ", input computer becomes
At " En En+I go to the cinema tomorrow "；Computer is it can be understood that at+response problem of watching movie tomorrow.In this manner into
And obtain more complete information.
Sufficiently the current session text information of analysis identification user and past text conversation information, analyze the factor of processing
It is more, it can make to extract obtained user feeling characteristic information and user's meaning of one's words characteristic information is more acurrate, and then provide robot
Answer it is more acurrate, it is more intelligent.
Specifically, in step S4, specifically:
Data bank is obtained, multiple answers are stored in advance in data bank；
According to user's meaning of one's words characteristic information, selected in data bank in conjunction with both sides' affective characteristics value by machine learning algorithm
One is selected to respond as the corresponding answer of current text information.
The answer that robot provides can select a correspondence from data bank by way of selecting and reasonably answer, and lead to
The related algorithm for crossing machine learning, the answer for providing robot are more intelligent.
Specifically, in step S4, specifically:
According to user's meaning of one's words characteristic information, automatically generates and work as by the method for logic rules in conjunction with both sides' affective characteristics value
The corresponding answer of preceding text information:
According to user semantic characteristic information, emotional characteristics in semantic characteristic information are analyzed；
A kind of logical combination is formed in conjunction with the mood of robot according to emotional characteristics in semantic feature information；
According to logical combination, current text is automatically generated from the answer strategy that the method by logic rules pre-establishes
The corresponding answer of information.
Citing is illustrated:
User's input: " I and boyfriend say good-bye ", the meaning of one's words is characterized in: " saying good-bye, boyfriend ", user emotion feature meeting
It is biased to " sad ", it is assumed that the mood of robot at this time is " neutrality ", then having higher chance to answer, " I can understand your present sense
Feel, I can accompany you chat chat " etc. comfort type reply.If robot emotion is " cold and detached ", most probably respond: " En En refuels
~".
Logic rules among these refer to: various groups of " meaning of one's words feature " and " both sides' mood " that we have pre-established
It closes, if the logical combination that " meaning of one's words feature " and " both sides' mood " in current session content is formed, which drops into, passes through logic rules
Certain combination of the answer strategy that pre-establishes of method when, one response will be selected in certain answer scope.By patrolling
The method for collecting rule carries out human-computer dialogue, to the answer that user is quick, intelligent, while considering the mood of both sides, makes to answer more intelligence
Can, it is more humane.
Specifically, in step S4, specifically:
According to user's meaning of one's words characteristic information, automatically generates and work as by the method for machine learning in conjunction with both sides' affective characteristics value
The corresponding answer of preceding text information:
According to user semantic characteristic information, emotional characteristics in semantic characteristic information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of robot according to emotional characteristics in semantic feature information；
According to Response Policy, life is automatically generated or selected from the answer strategy that the method by machine learning pre-establishes
At the corresponding answer of current text information.
For example:
User's input: " I and boyfriend say good-bye ", the meaning of one's words is characterized in: " saying good-bye, boyfriend ", user emotion feature meeting
It is biased to " sad ", it is assumed that the mood of robot at this time is " neutrality ", then having higher chance to answer, " I can understand your present sense
Feel, I can accompany you chat chat " etc. comfort type reply.If robot emotion is " cold and detached ", most probably respond: " En En refuels
~".
Machine learning algorithm among these is to allow machine learning " meaning of one's words feature " and " both sides through prior data collection
Collocation of the mood " with " automatically generating response " or " how selection is responded ".
It responds feedback according to the practical response situation of every user and experiment in training data to compare, if comparing result
It is inconsistent, then update the parameter of machine learning, and repeat iteration, when in training data the response that generates with it is anti-with experiment
It is fed back to when answering consistency up to highest, then stops iteration, and training is completed.In the stage of test, when user inputs one
When this paper sentence, we can through passing data train come prediction module come responded (can automatically generate or
Person, which is that selection is suitable, to be responded).
Answer strategy is pre-established by the method for machine learning, if " meaning of one's words feature " that includes in current text information
The answer strategy that " both sides' mood " is formed is matched with some in the answer strategy pre-established, then can be automatically generated current
The corresponding answer of text information.Answer strategy if the answer strategy formed does not rain in the answer strategy pre-established
Match, then the corresponding answer of similar answer strategy generating current text information may be selected.By the method for machine learning, can be obtained
A large amount of and accurate answer strategy provides the answer of accurate intelligence for user.
Embodiment two
Fig. 2 shows a kind of systems answered based on affective characteristics adjustment robot provided by second embodiment of the invention
Schematic diagram.As shown in Fig. 2, second embodiment of the present invention provides a kind of systems answered based on affective characteristics adjustment robot
10, comprising:
Text information obtains module 101, for obtaining the current text information and past text information of user's input；
Talk with identification module 102, for extracting the feature letter of user according to current text information and past text information
Breath, characteristic information includes user feeling characteristic information and user's meaning of one's words characteristic information；
Robot emotion module 103, for obtaining robot affective characteristics information, in conjunction with user feeling characteristic information, into
Row information integration, obtains both sides' affective characteristics value, and robot affective characteristics information is the information warp interacted by the past with user
Calculate acquisition；
Talk with response means 104, in conjunction with both sides' affective characteristics value, providing current according to user's meaning of one's words characteristic information
The corresponding answer of text information.
A kind of system 10 answered based on affective characteristics adjustment robot provided by the invention, technical solution are as follows: first lead to
It crosses text information and obtains module 101, for obtaining the current text information and past text information of user's input；Then by pair
Identification module 102 is talked about, for extracting the characteristic information of user, feature letter according to current text information and past text information
Breath includes user feeling characteristic information and user's meaning of one's words characteristic information；Then by robot emotion module 103, for obtaining machine
Device human feelings sense characteristic information carries out information integration, obtains both sides' affective characteristics value, robot in conjunction with user feeling characteristic information
Affective characteristics information is that the information interacted by the past with user is computed acquisition；Finally by dialogue response means 104, use
In providing the corresponding answer of current text information in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information.
The of the invention system 10 answered based on affective characteristics adjustment robot, based on during human-computer dialogue ought be above
This information and in the past text information, extract user feeling characteristic information and robot affective characteristics information, in conjunction with user's meaning of one's words
Characteristic information, the answer for providing robot have the emotion of robot, and human-computer dialogue is more intelligent, further improves use
Family experience.
Specifically, talk with identification module 102, be specifically used for:
According to current text information and past text information, the mood in current text information and in the past text information is obtained
Status information, emotional state information include current emotional states information, user's past main emotional information and past secondary mood
At least one of information；
According to current text information and past text information, the text in current text information and in the past text information is obtained
Message, text message includes current text information and the semantics information in text information, key word information, proper noun are believed in the past
At least one of breath and verb information；
According to current text information and past text information, the topic in current text information and in the past text information is obtained
Information, topic information includes current staple of conversation information, currently secondary topic information, past staple of conversation information, past are secondary
Topic information likes topic information and at present at least one of hot topic information；
According to current text information and past text information, the sentence in current text information and in the past text information is obtained
Type, tone information and language performance information, language performance information include current dominant language behavioural information and secondary language performance
Information；
According to emotional state information, text message, topic information, sentence pattern, tone information and language performance information, extract
The characteristic information of user, characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information.
Current text information combination contextual information can also be rewritten the information of the rewriting sentence obtained after original sentence
Be added to extract characteristic information source information in, with emotional state information, text message, topic information, sentence pattern, tone information and
Language performance information extracts the characteristic information of user together.
Wherein, the information for rewriting sentence is phenomenon in order to solve some sentence information deficiencies.
Such as:
Do A: you like watching movie?
B: like.
But B merely enters " liking ", information content is insufficient at this time." like seeing if just will become according to rewriting above
Film ", computer can make more accurate judgement at this time.
Original sentence: retain the appearance of original sentence, computer capacity is allowed to access original sentence.Sentence is rewritten in cooperation, more
It is fully understood by user's input.
Such as:
A: we will go to the cinema tomorrow
B: En En.
At this point, " En En " can be rewritten into " I will go to the cinema tomorrow ".If retaining former sentence " En En ", input computer becomes
At " En En+I go to the cinema tomorrow "；Computer is it can be understood that at+response problem of watching movie tomorrow.In this manner into
And obtain more complete information.
Sufficiently the current session text information of analysis identification user and past text conversation information, analyze the factor of processing
It is more, it can make to extract obtained user feeling characteristic information and user's meaning of one's words characteristic information is more acurrate, and then provide robot
Answer it is more acurrate, it is more intelligent.
Specifically, talk with response means 104, be specifically used for:
Data bank is obtained, multiple answers are stored in advance in data bank；
According to user's meaning of one's words characteristic information, selected in data bank in conjunction with both sides' affective characteristics value by machine learning algorithm
One is selected to respond as the corresponding answer of current text information.
The answer that robot provides can select a correspondence from data bank by way of selecting and reasonably answer, and lead to
The related algorithm for crossing machine learning, the answer for providing robot are more intelligent.
Specifically, talk with response means 104, be specifically used for:
According to user's meaning of one's words characteristic information, automatically generates and work as by the method for logic rules in conjunction with both sides' affective characteristics value
The corresponding answer of preceding text information:
According to user semantic characteristic information, emotional characteristics in semantic characteristic information are analyzed；
A kind of logical combination is formed in conjunction with the mood of robot according to emotional characteristics in semantic feature information；
According to logical combination, current text is automatically generated from the answer strategy that the method by logic rules pre-establishes
The corresponding answer of information.
Citing is illustrated:
User's input: " I and boyfriend say good-bye ", the meaning of one's words is characterized in: " saying good-bye, boyfriend ", user emotion feature meeting
It is biased to " sad ", it is assumed that the mood of robot at this time is " neutrality ", then having higher chance to answer, " I can understand your present sense
Feel, I can accompany you chat chat " etc. comfort type reply.If robot emotion is " cold and detached ", most probably respond: " En En refuels
~".
Logic rules among these refer to: various groups of " meaning of one's words feature " and " both sides' mood " that we have pre-established
It closes, if the logical combination that " meaning of one's words feature " and " both sides' mood " in current session content is formed, which drops into, passes through logic rules
Certain combination of the answer strategy that pre-establishes of method when, one response will be selected in certain answer scope.By patrolling
The method for collecting rule carries out human-computer dialogue, to the answer that user is quick, intelligent, while considering the mood of both sides, makes to answer more intelligence
Can, it is more humane.
Specifically, talk with response means 104, be specifically used for:
According to user's meaning of one's words characteristic information, automatically generates and work as by the method for machine learning in conjunction with both sides' affective characteristics value
The corresponding answer of preceding text information:
According to user semantic characteristic information, emotional characteristics in semantic characteristic information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of robot according to emotional characteristics in semantic feature information；
According to Response Policy, life is automatically generated or selected from the answer strategy that the method by machine learning pre-establishes
At the corresponding answer of current text information.
Equally for example:
User's input: " I and boyfriend say good-bye ", the meaning of one's words is characterized in: " saying good-bye, boyfriend ", user emotion feature meeting
It is biased to " sad ", it is assumed that the mood of robot at this time is " neutrality ", then having higher chance to answer, " I can understand your present sense
Feel, I can accompany you chat chat " etc. comfort type reply.If robot emotion is " cold and detached ", most probably respond: " En En refuels
~".
Machine learning algorithm among these is to allow machine learning " meaning of one's words feature " and " both sides through prior data collection
Collocation of the mood " with " automatically generating response " or " how selection is responded ".
It responds feedback according to the practical response situation of every user and experiment in training data to compare, if comparing result
It is inconsistent, then update the parameter of machine learning, and repeat iteration, when in training data the response that generates with it is anti-with experiment
It is fed back to when answering consistency up to highest, then stops iteration, and training is completed.In the stage of test, when user inputs one
When this paper sentence, we can through passing data train come prediction module come responded (can automatically generate or
Person, which is that selection is suitable, to be responded).
Answer strategy is pre-established by the method for machine learning, if " meaning of one's words feature " that includes in current text information
The answer strategy that " both sides' mood " is formed is matched with some in the answer strategy pre-established, then can be automatically generated current
The corresponding answer of text information.Answer strategy if the answer strategy formed does not rain in the answer strategy pre-established
Match, then the corresponding answer of similar answer strategy generating current text information may be selected.By the method for machine learning, can be obtained
A large amount of and accurate answer strategy provides the answer of accurate intelligence for user；The answer of robot, can be according to current " meaning of one's words, use
Family emotion, robot emotion " responds.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme should all cover within the scope of the claims and the description of the invention.
Claims (8)
1. the method answered based on affective characteristics adjustment robot characterized by comprising
Step S1 obtains the current text information and past text information of user's input；
Step S2 extracts the characteristic information of the user, the spy according to the current text information and past text information
Reference breath includes user feeling characteristic information and user's meaning of one's words characteristic information；
Step S3 obtains robot affective characteristics information, in conjunction with the user feeling characteristic information, carries out information integration, obtains
Both sides' affective characteristics value, the robot affective characteristics information are that the information interacted by the past with the user is computed acquisition
's；
Step S4 provides the current text letter in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
Cease corresponding answer；
The step S2, specifically:
According to the current text information and past text information, obtain in the current text information and in the past text information
Emotional state information, the emotional state information include current emotional states information, user's past main emotional information and past
At least one of secondary emotional information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Text message, the text message includes the current text information and the semantics information in text information, keyword are believed in the past
At least one of breath, proper noun information and verb information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Topic information, the topic information include current staple of conversation information, current secondary topic information, past staple of conversation information,
Past secondary topic information likes topic information and at present at least one of hot topic information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Sentence pattern, tone information and language performance information, the language performance information include current dominant language behavioural information and secondary language
Say behavioural information；
According to the emotional state information, text message, topic information, sentence pattern, tone information and language performance information, extract
The characteristic information of the user, the characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information.
2. the method according to claim 1 answered based on affective characteristics adjustment robot, which is characterized in that
In the step S4, specifically:
Data bank is obtained, multiple answers are stored in advance in the data bank；
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by machine learning algorithm, in the money
One is selected to respond as the corresponding answer of the current text information in material library.
3. the method according to claim 1 answered based on affective characteristics adjustment robot, which is characterized in that
In the step S4, specifically:
It is automatic raw by the method for logic rules in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
At the corresponding answer of the current text information:
According to user's meaning of one's words characteristic information, emotional characteristics in the meaning of one's words characteristic information are analyzed；
A kind of logical combination is formed in conjunction with the mood of the robot according to emotional characteristics in the meaning of one's words characteristic information；
According to the logical combination, automatically generated from the answer strategy that the method by logic rules pre-establishes described current
The corresponding answer of text information.
4. the method according to claim 1 answered based on affective characteristics adjustment robot, which is characterized in that
In the step S4, specifically:
It is automatic raw by the method for machine learning in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
At the corresponding answer of the current text information:
According to user's meaning of one's words characteristic information, emotional characteristics in the meaning of one's words characteristic information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of the robot according to emotional characteristics in the meaning of one's words characteristic information；
According to the Response Policy, life is automatically generated or selected from the answer strategy that the method by machine learning pre-establishes
At the corresponding answer of the current text information.
5. the system answered based on affective characteristics adjustment robot characterized by comprising
Text information obtains module, for obtaining the current text information and past text information of user's input；
Talk with identification module, for extracting the feature of the user according to the current text information and past text information
Information, the characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information；
Robot emotion module, in conjunction with the user feeling characteristic information, carries out letter for obtaining robot affective characteristics information
Breath integration, obtains both sides' affective characteristics value, the robot affective characteristics information is the letter interacted by the past with the user
Breath is computed acquisition；
Talk with response means, in conjunction with both sides' affective characteristics value, providing described according to user's meaning of one's words characteristic information
The corresponding answer of current text information；
The dialogue identification module, is specifically used for:
According to the current text information and past text information, obtain in the current text information and in the past text information
Emotional state information, the emotional state information include current emotional states information, user's past main emotional information and past
At least one of secondary emotional information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Text message, the text message includes the current text information and the semantics information in text information, keyword are believed in the past
At least one of breath, proper noun information and verb information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Topic information, the topic information include current staple of conversation information, current secondary topic information, past staple of conversation information,
Past secondary topic information likes topic information and at present at least one of hot topic information；
According to the current text information and past text information, obtain in the current text information and in the past text information
Sentence pattern, tone information and language performance information, the language performance information include current dominant language behavioural information and secondary language
Say behavioural information；
According to the emotional state information, text message, topic information, sentence pattern, tone information and language performance information, extract
The characteristic information of the user, the characteristic information include user feeling characteristic information and user's meaning of one's words characteristic information.
6. the system according to claim 5 answered based on affective characteristics adjustment robot, which is characterized in that
The dialogue response means, are specifically used for:
Data bank is obtained, multiple answers are stored in advance in the data bank；
According to user's meaning of one's words characteristic information, in conjunction with both sides' affective characteristics value, by machine learning algorithm, in the money
One is selected to respond as the corresponding answer of the current text information in material library.
7. the system according to claim 5 answered based on affective characteristics adjustment robot, which is characterized in that
The dialogue response means, are specifically used for:
It is automatic raw by the method for logic rules in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
At the corresponding answer of the current text information:
According to user's meaning of one's words characteristic information, emotional characteristics in the meaning of one's words characteristic information are analyzed；
A kind of logical combination is formed in conjunction with the mood of the robot according to emotional characteristics in the meaning of one's words characteristic information；
According to the logical combination, automatically generated from the answer strategy that the method by logic rules pre-establishes described current
The corresponding answer of text information.
8. the system according to claim 5 answered based on affective characteristics adjustment robot, which is characterized in that
The dialogue response means, are specifically used for:
It is automatic raw by the method for machine learning in conjunction with both sides' affective characteristics value according to user's meaning of one's words characteristic information
At the corresponding answer of the current text information:
According to user's meaning of one's words characteristic information, emotional characteristics in the meaning of one's words characteristic information are analyzed；
A kind of Response Policy is formed in conjunction with the mood of the robot according to emotional characteristics in the meaning of one's words characteristic information；
According to the Response Policy, life is automatically generated or selected from the answer strategy that the method by machine learning pre-establishes
At the corresponding answer of the current text information.
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| CN107329986A (en) * | 2017-06-01 | 2017-11-07 | 竹间智能科技（上海）有限公司 | The interactive method and device recognized based on language performance |
| CN107301168A (en) * | 2017-06-01 | 2017-10-27 | 深圳市朗空亿科科技有限公司 | Intelligent robot and its mood exchange method, system |
| CN107423364B (en) * | 2017-06-22 | 2024-01-26 | 百度在线网络技术（北京）有限公司 | Method, device and storage medium for answering operation broadcasting based on artificial intelligence |
| CN109202922B (en) * | 2017-07-03 | 2021-01-22 | 北京光年无限科技有限公司 | Emotion-based man-machine interaction method and device for robot |
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| CN108053826B (en) * | 2017-12-04 | 2021-01-15 | 泰康保险集团股份有限公司 | Method and device for man-machine interaction, electronic equipment and storage medium |
| US10522143B2 (en) * | 2018-02-27 | 2019-12-31 | Microsoft Technology Licensing, Llc | Empathetic personal virtual digital assistant |
| CN109129467B (en) * | 2018-07-27 | 2022-03-25 | 南京阿凡达机器人科技有限公司 | Robot interaction method and system based on cognition |
| CN111199732B (en) | 2018-11-16 | 2022-11-15 | 深圳Tcl新技术有限公司 | Emotion-based voice interaction method, storage medium and terminal equipment |
| CN111310460B (en) * | 2018-12-12 | 2022-03-01 | Tcl科技集团股份有限公司 | Statement adjusting method and device |
| CN110675853B (en) * | 2019-09-10 | 2022-07-05 | 苏宁云计算有限公司 | Emotion voice synthesis method and device based on deep learning |
| CN112000781B (en) * | 2020-07-20 | 2023-07-21 | 北京百度网讯科技有限公司 | Information processing method and device in user dialogue, electronic equipment and storage medium |
| CN113076407B (en) * | 2021-03-22 | 2023-07-21 | 联想(北京)有限公司 | Information processing method and device |
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