CN110059231A

CN110059231A - A kind of generation method and device of reply content

Info

Publication number: CN110059231A
Application number: CN201910322333.9A
Authority: CN
Inventors: 黄海生
Original assignee: Media (guangzhou) Technology Co Ltd
Priority date: 2019-04-19
Filing date: 2019-04-19
Publication date: 2019-07-26
Anticipated expiration: 2039-04-19
Also published as: CN110059231B

Abstract

The present invention relates to data analysis technique fields, disclose the generation method and device of a kind of reply content, comprising: obtain the person of telling tells content；The content of telling is analyzed, several is obtained and tells characterize data；Characterize data is told as foundation using several, the generation of mould group is generated by replying and tells reply content, is told reply content and is told characterize data with any one and matches.Implement the embodiment of the present invention, several told in content can be got and tell characterize data, and characterize data is told according to several and generates reply content, so that reply content is more bonded with the content of telling that user inputs, not only it had improved to the efficiency for telling content reply, and but also had improved the usage experience of user.

Description

A kind of generation method and device of reply content

Technical field

The present invention relates to data analysis technique fields, and in particular to a kind of generation method and device of reply content.

Background technique

With the development of internet, more and more people's selection, which pours out platform using emotion, will be unwilling to say to household or friend The thing of friend is told to stranger, and in order to which the content of telling to the person of telling is made and targetedly being replied, it is usual that emotion pours out platform The artificial mode of meeting replys the content of telling for telling people.However, it has been found in practice that by artificial mode to telling The efficiency that content is replied is lower.

Summary of the invention

The embodiment of the present invention discloses the generation method and device of a kind of reply content, is able to ascend and returns to telling content Multiple efficiency improves user experience.

First aspect of the embodiment of the present invention discloses a kind of generation method of reply content, which comprises

Obtain the person of telling tells content；

The content of telling is analyzed, several is obtained and tells characterize data, the characterize data of telling at least is wrapped Containing telling text data；

Tell characterize data as foundation using described several, by reply generate mould group generate described in tell content corresponding Reply content, the reply content are matched with any one of characterize data of telling.

As an alternative embodiment, in first aspect of the embodiment of the present invention, it is described to it is described tell content into Row analysis, obtains several and tells characterize data, comprising:

Tell the current data type of content described in determination, the data type include at least text type, audio types, Any one in picture type and video type；

When the current data type is the text type, by several text content analysis models to institute's statement It states content to be analyzed, obtains that each text content analysis model is corresponding to tell characterize data；

When the current data type is any in the audio types, the picture type and the video type When a kind of, the content of telling is analyzed by several content analysis models, obtains each content analysis model Corresponding text tells characterize data, and tells characterize data to the text by several text content analysis models and carry out Analysis, obtains that each text content analysis model is corresponding to tell characterize data.

As an alternative embodiment, in first aspect of the embodiment of the present invention, it is described to be told with described several Characterize data is foundation, tells the corresponding reply content of content described in the generation of mould group by replying to generate, comprising:

Determine each data type for telling characterize data；

The determining and described matched reply generation model of data type for telling characterize data in mould group is generated from replying, In, the data type that characterize data is told described in one matches the reply and generates model；

Characterize data input and the matched reply generation of the data type for telling characterize data are told by described In model；

It is generated from reply described in several and tells the corresponding reply content of content described in obtaining in model.

As an alternative embodiment, in first aspect of the embodiment of the present invention, it is described to tell characterization number for described It is generated in model according to input and the matched reply of the data type for telling characterize data, comprising:

By it is each it is described tell characterize data input respectively it is matched described with the data type for telling characterize data It replys and generates in model；Alternatively,

It tells characterize data by described and is combined according to preset data built-up pattern, obtain several targets and tell characterization number According to, and obtain the target data type that each target tells characterize data；

It is matched that the target is told into the target data type that characterize data input tells characterize data with the target It replys and generates in model.

As an alternative embodiment, in first aspect of the embodiment of the present invention, it is described from reply described in several It generates and tells the corresponding reply content of content described in obtaining in model, comprising:

Several revertant contents are obtained from several described described reply models, wherein in a revertant Hold from a reply model；

Several described revertant contents are integrated, obtain described telling the corresponding reply content of content.

As an alternative embodiment, in first aspect of the embodiment of the present invention, it is described by several replies Sub- content is integrated, and obtains described telling the corresponding reply content of content, comprising:

The quantity of the corresponding revertant content of content is told described in detection；

When the quantity for detecting the revertant content is multiple, according to default reply built-up pattern by each described time Multiple sub- content is combined, and obtains described telling the corresponding reply content of content；

When the quantity for detecting the revertant content is one, the revertant content is determined as in described tell Hold corresponding reply content.

As an alternative embodiment, the basis is default to reply combination in first aspect of the embodiment of the present invention Each revertant content is combined by model, obtains described telling the corresponding reply content of content, comprising:

Each revertant content is combined according to default reply built-up pattern, obtains initial reply content；

Operation is optimized to the initial reply content, obtain it is final it is described tell the corresponding reply content of content, Wherein, the optimization operation includes at least grammatically wrong sentence amendment operation and/or replacement/deletion sensitive word operation.

Second aspect of the embodiment of the present invention discloses a kind of generating means of reply content, comprising:

Acquiring unit, obtain the person of telling tells content；

Analytical unit obtains several and tells characterize data for analyzing the content of telling, described to tell table Sign data, which include at least, tells text data；

Generation unit is generated described in the generation of mould group for telling characterize data as foundation using described several by replying The corresponding reply content of content is told, the reply content is matched with any one of characterize data of telling.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the analytical unit includes:

First determines subelement, and for telling the current data type of content described in determination, the data type is at least wrapped Include any one in text type, audio types, picture type and video type；

First analysis subelement, for passing through several texts when the current data type is the text type Content analysis model analyzes the content of telling, and obtains that each text content analysis model is corresponding to tell characterization Data；

Second analysis subelement, for when the current data type be the audio types, the picture type and When any one in the video type, the content of telling is analyzed by several content analysis models, is obtained The corresponding text of each content analysis model tells characterize data, and by several text content analysis models to described Text is told characterize data and is analyzed, and obtains that each text content analysis model is corresponding to tell characterize data.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the generation unit includes:

Second determines subelement, for determining each data type for telling characterize data；

Third determines subelement, for generating the determining and data type for telling characterize data in mould group from reply The reply matched generates model, wherein the data type that characterize data is told described in one matches the reply and generates model；

Subelement is inputted, for telling characterize data input by described and matching with the data type for telling characterize data The reply generate model in；

Subelement is obtained, obtains described tell in the corresponding reply of content in model for generating from reply described in several Hold.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the input subelement includes:

First input module, for inputting and the number for telling characterize data each characterize data of telling respectively It is generated in model according to the reply of type matching；Alternatively,

Composite module obtains several for telling characterize data by described and being combined according to preset data built-up pattern Target tells characterize data, and obtains the target data type that each target tells characterize data；

Second input module tells the mesh of characterize data with the target for the target to be told characterize data input The matched reply of data type is marked to generate in model.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the acquisition subelement includes:

Module is obtained, for obtaining several revertant contents from several described described reply models, wherein one The revertant content is from a reply model；

Module is integrated, for integrating several described revertant contents, obtains described tell content corresponding time Multiple content.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the module of integrating includes:

Detection sub-module, for detecting the quantity for telling the corresponding revertant content of content；

Submodule is combined, for being combined according to default reply when the quantity for detecting the revertant content is multiple Each revertant content is combined by model, obtains described telling the corresponding reply content of content；

Determine submodule, for when detect the revertant content quantity be one when, by the revertant content The corresponding reply content of content is told described in being determined as.

As an alternative embodiment, in second aspect of the embodiment of the present invention, the combination submodule includes:

Combine component, for when detect the revertant content quantity be it is multiple when, according to default reply combination die Each revertant content is combined by type, obtains initial reply content；

Optimization component obtains final described telling content pair for optimizing operation to the initial reply content The reply content answered, wherein the optimization operation includes at least grammatically wrong sentence amendment operation and/or replacement/deletion sensitive word operation.

The third aspect of the embodiment of the present invention discloses a kind of electronic equipment, comprising:

It is stored with the memory of executable program code；

The processor coupled with the memory；

The processor calls the executable program code stored in the memory, executes any of first aspect A kind of some or all of method step.

Fourth aspect of the embodiment of the present invention discloses a kind of computer readable storage medium, the computer readable storage medium Store program code, wherein said program code includes the part or complete for executing any one method of first aspect The instruction of portion's step.

The 5th aspect of the embodiment of the present invention discloses a kind of computer program product, when the computer program product is calculating When being run on machine, so that the computer executes some or all of any one method of first aspect step.

The aspect of the embodiment of the present invention the 6th disclose a kind of using distribution platform, and the application distribution platform is for publication calculating Machine program product, wherein when the computer program product is run on computers, so that the computer executes first party Some or all of any one method in face step.

Compared with prior art, the embodiment of the present invention has the advantages that

In the embodiment of the present invention, the content for the person of telling is obtained；The content of telling is analyzed, several are obtained Tell characterize data；Characterize data is told as foundation using several, is told reply content by replying generation mould group generation, is told Reply content is told characterize data with any one and is matched.As it can be seen that implementing the embodiment of the present invention, it can get and tell in content Several tell characterize data, and tell characterize data according to several and generate reply content, so that reply content and user The content of telling of input is more bonded, and has not only been improved to the efficiency for telling content reply, but also has been improved the usage experience of user.

Detailed description of the invention

It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to needed in the embodiment Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached Figure.

Fig. 1 is a kind of flow diagram of the generation method of reply content disclosed by the embodiments of the present invention；

Fig. 2 is the flow diagram of the generation method of another reply content disclosed by the embodiments of the present invention；

Fig. 3 is the flow diagram of the generation method of another reply content disclosed by the embodiments of the present invention；

Fig. 4 is a kind of structural schematic diagram of the generating means of reply content disclosed by the embodiments of the present invention；

Fig. 5 is the structural schematic diagram of the generating means of another reply content disclosed by the embodiments of the present invention；

Fig. 6 is the structural schematic diagram of the generating means of another reply content disclosed by the embodiments of the present invention；

Fig. 7 is the structural schematic diagram of a kind of electronic equipment disclosed by the embodiments of the present invention.

Specific embodiment

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on this Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts Example is applied, shall fall within the protection scope of the present invention.

It should be noted that term " includes " and " having " and their any changes in the embodiment of the present invention and attached drawing Shape, it is intended that cover and non-exclusive include.Such as contain the process, method of a series of steps or units, system, product or Equipment is not limited to listed step or unit, but optionally further comprising the step of not listing or unit or optional Ground further includes the other step or units intrinsic for these process, methods, product or equipment.

The embodiment of the present invention discloses the generation method and device of a kind of reply content, and reply content and user can be made to input Content of telling more be bonded, not only improved to the efficiency for telling content reply, but also improved the usage experience of user.Divide below It is not described in detail.

Embodiment one

Referring to Fig. 1, Fig. 1 is a kind of flow diagram of the generation method of reply content disclosed by the embodiments of the present invention. As shown in Figure 1, the generation method of the reply content may comprise steps of:

101, electronic equipment obtains the person of telling and tells content.

102, electronic equipment is analyzed content is told, and is obtained several and is told characterize data, tells characterize data extremely Few includes to tell text data.

In the embodiment of the present invention, electronic equipment can be the generating means of reply content.The type for telling content can be The types such as text, picture, audio and video, the person of telling can by electronic equipment export page input text type and/ Or picture type tells content, can also pass through sound collection equipment (such as microphone) input audio type of electronic equipment Tell content (such as user input tell voice), the image capture device (such as camera) of electronic equipment can also be passed through The video type of user's input is acquired simultaneously with sound collection equipment and audio types tell content.Telling content can only wrap It can also simultaneously include a plurality of types of contents containing a type of content.

In the embodiment of the present invention, telling content includes telling content body and/or telling content title, tells content body It can be and tell content body text, be also possible to tell content voice and tell (voice for such as not telling person is told), be also possible to Image content expression (such as the personal story drawn a picture with a group picture), is also possible to telling (as camera is shot for video capture The person's of telling tells audio-video), tell the one or more that content body may include above content；Telling content can be with can Selection of land includes the person's of telling personal information, and the person's of telling personal information pre-saves in the electronic device, as telling when use Content protion.

In the embodiment of the present invention, electronic equipment can be analyzed collected content of telling, and obtain one or more Tell characterize data；Electronic equipment can tell content and analyzed in different ways according to different types of, to obtain Final tells characterize data, since the reply content that electronic equipment is subsequently generated needs to be analyzed according to content of text, because It will include at least text data that this electronic equipment obtained, which tells in characterize data, so that the reply content that electronic equipment generates is more It is accurate to add.

In the embodiment of the present invention, telling characterize data may include the text for telling content, title, theme, abstract, label (such as tag along sort, topic label) tells intention, context events, disease information, people information and character relation information, tells The person's of stating personal information (such as age, gender, Social Identity, educational background, race, nationality, location, marital status), the person of telling Psychological analysis result tells style information, metaphor information, story fable information, religion information, social information (such as work letter Breath, learning information, life information etc.), scientific information (such as mathematical material, philosophy content, physics content, chemical content) with And semantic analysis result (such as statement entity, syntactic information, syntactic information, grammatical information)；Wherein, the quantity of text can have Multiple, the quantity of label can have multiple, and the quantity for telling the context events that content includes can have multiple, and the quantity of theme can Multiple to have, disease information can have multiple, and people information and character relation information can have multiple, and metaphor information can have more A, story fable information can have multiple, and religion information can have multiple, and social information can have multiple, and scientific information can be with Have multiple, the psychological analysis result for the person of telling can include at least cognitive analysis result, personality analysis result, character analysis knot It is any one in fruit, mood analysis result, subconsciousness analysis result, imagery analysis result and mental-psychological assessment result It is a.

In the embodiment of the present invention, each characterize data of telling is obtained by corresponding content analysis model, including, tell table Telling content text and can be directly obtained by telling content in sign data, or by identification model from telling content sound Frequently, tell content picture, tell audio content acquisition；Telling content title can be directly obtained by telling content (in telling Holding includes title)；The theme for telling content can generate model by theme and obtain；The abstract for telling content can pass through abstract Model is generated to obtain；The label for telling content can generate model by label and obtain；The person's of telling input is told in content Telling intention can be intended to generate model acquisition by telling；Telling the context events that content includes can be divided by context events Model is analysed to obtain；Model acquisition can be obtained by disease content by telling the disease information that content includes；Tell what content included People information and character relation information can be obtained by personage and character relation analysis model；The psychological analysis knot for the person of telling Fruit can be obtained by psychological analysis model；Telling content and telling style information can be obtained by telling style analysis model ?；Telling the metaphor information that content includes can be obtained by metaphor analysis model；Tell the story fable information that content includes It can be obtained by story fable analysis model；Telling the religion information that content includes can be obtained by Religion content analysis model ?；Telling the social information that content includes can be obtained by social information's analysis model；Tell the scientific information that content includes It can be obtained by scientific contents analysis model；Telling contents semantic analysis result can be obtained by semantic module；It tells The person's of stating personal information can be obtained by telling content, can also be obtained by personal information analysis module.In addition, theme generates Model, summarization generation model, label generate model, tell intention generation model, context events analysis model, the acquisition of disease content Model, personage and character relation analysis model tell style analysis model, metaphor analysis model, story fable analysis model, ancestor Religion content analysis model, social information's analysis model, scientific contents analysis model and psychological analysis model can pass through Nerual network technique building based on machine learning；Semantic module can be constructed by traditional semantic analysis technology, or It is constructed by the neural network model based on machine learning.In addition, in recent years, the neural network mould based on machine learning Type has in the field natural language processing (Natural Language Processing, NLP) to be widely applied, and is plucked in text Generate, machine read understand, mood analysis, content generate etc. have research achievement abundant, these research achievements are The response answering system of building automation provides theory and technology basis.

Wherein, content text, title, theme, abstract, label, intention, context events, disease information, people information are told It can be content of text with character relation information, the person's of telling personal information, metaphor information, story fable information, scientific information； Above-mentioned religion information, psychological analysis result, tells style information, semantic analysis result at social information, can be textual data According to being also possible to numeric data.

Wherein, characterize data or a text content analysis mould are poured out for available one, a content analysis model Type is also available multiple to pour out characterize data.

As an alternative embodiment, electronic equipment analyzes the content of telling for getting the person of telling, obtain Several modes for telling characterize data may include following steps:

Electronic equipment analyzes the content of telling for getting the person of telling, and determines the data type for telling content；

When detecting the data type for telling content is text type, electronic equipment passes through the corresponding content of text type Analysis model analyzes the content of telling of text type, obtains several and tells characterize data；

When detecting the data type for telling content is picture type, electronic equipment passes through graph image identification model pair The content of telling of picture type carries out discriminance analysis, the available text information told in content picture, and passes through text class The corresponding content analysis model of type analyzes text information, obtains several and tells characterize data, can also identify When telling in content comprising facial image of picture type, the identification based on human facial expression recognition model to facial image obtains The psychology characterize data for the person of telling, and can will tell text data and psychology characterize data is determined as identifying jointly To several tell characterize data；

When detecting the data type for telling content is audio types, electronic equipment is by being based on speech recognition technology The speech recognition module of (Automatic Speech Recognition, ASR) is identified to content is told, and obtains telling interior Hold the corresponding text information of voice, and then text information is analyzed by text type corresponding content analysis model, obtains Characterize data is told to several, is also based on sound Emotion identification model and identification point is carried out to the person's of telling sound of speaking feature Analysis, obtains the psychology characterize data for the person of telling；

When detecting the data type for telling content is video type, electronic equipment is by speech recognition module to telling Audio-frequency information in content is identified, the text information for telling content is obtained, can also be by based on limbs Activity recognition point It analyses model and the analysis of subconsciousness Activity recognition is carried out to the limbs behavior of personage, obtain the psychology characterize data for the person of telling, and All text informations are analyzed by the corresponding content analysis model of text type, several is obtained and tells characterization number According to.

Wherein, implement this embodiment, can according to different types of data tell content using different modes into Row analysis, the corresponding difference of content of telling for obtaining different types of data tell characterize data, improve analysis and obtain telling table Levy the accuracy of data.

Optionally, when detecting the data type for telling content is picture type, electronic equipment can be by image Hold analysis model and discriminance analysis is carried out to the content of telling of picture type, can analyze to obtain telling in content for picture type People event's content, and can be analyzed from people event's content and obtain the content description for telling content to image type, And then it generates and describes corresponding body text information with the content, and pass through the corresponding text content analysis model pair of text type Text information is analyzed, obtain several and tell characterize data, to improve electronic equipment for the content of image type The diversity of analysis mode.

103, electronic equipment tells characterize data as foundation using several, tells content pair by replying generation mould group generation The reply content answered, reply content are told characterize data with any one and are matched.

In the embodiment of the present invention, replying generation mould group can be constructed by neural network model, can also optionally include Traditional NLP semantic analysis technology module, electronic equipment can generate mould group and generate and each to tell characterize data equal by replying Matched reply content, to obtain telling the matched content of characterize data with any one in the reply content of generation.

For example, the text for telling content of user's input can be with are as follows: " I makes great efforts always very much, and the time after school all exists Study, achievement or fluctuated, current examination is again not have good mark.Why others is playing the time after school, but total marks of the examination But better than me.I does not understand in face of those outstanding persons what existing meaning the people of my this mediocrity has.It is top in face of society Elite, what the people of numerous mediocrities living in this purpose is in the world? ", electronic equipment can analyze by plurality of kinds of contents Model analyzes the text for telling content, can when being analyzed by theme generation model the text for telling content With from tell theme that content is told obtained in the text of content it is corresponding tell characterize data be " in face of elite, I this such as What receives the mediocrity of oneself? ", can be from telling when being analyzed by context events analysis model the text for telling content The corresponding characterize data of telling of situation that content is told obtained in the text of content is stated as " I makes great efforts always very much, when extracurriliar Between all learning, achievement or fluctuated, current examination is again not have good mark " and " why others is playing the time after school, but Total marks of the examination are but better than me ".Further, electronic equipment can generation mould group generation is corresponding with situation to tell table by replying " this scene that you say: ' why others is playing the time after school first revertant content of sign data, but total marks of the examination It is better than me ', this is too common situation, and effort, which is settled, does not obtain best result "；Electronic equipment can also be generated by replying Mould group generates the second revertant content for telling characterize data corresponding with theme, and " asking first is the attitude of life, rather than people Raw meaning.Big tree has the scene set greatly, and small grass has the excellent of small grass, this is only the phychology of health ".Electronic equipment can pass through It replys generation model to be combined the first revertant content and the second revertant content, obtaining final reply content, " you say This scene: ' why others is playing the time after school, but total marks of the examination are better than me ', this is too common situation, is exerted Power, which is settled, does not obtain best result.My answer is: asking first is the attitude of life, rather than the meaning of life.Big tree has The scene set greatly, small grass have the excellent of small grass, this is only the phychology of health ".The final reply that electronic equipment can will obtain Content is exported, so that the person of telling views electronic equipment and is directed to the reply for telling content, so that electronic equipment exports Reply content more there is specific aim.

In the method depicted in fig. 1, reply content can be made more to be bonded with the content of telling that user inputs, both promoted To telling the efficiency of content reply, and improve the usage experience of user.In addition, implementing method described in Fig. 1, improve Analysis obtains telling the accuracy of characterize data.In addition, implementing method described in Fig. 1, electronic equipment is improved for image class The diversity of the analysis mode of the content of type.In addition, implementing method described in Fig. 1, so that in the reply of electronic equipment output Holding more has specific aim.

Embodiment two

Referring to Fig. 2, Fig. 2 is the process signal of the generation method of another reply content disclosed by the embodiments of the present invention Figure.As shown in Fig. 2, the generation method of the reply content may comprise steps of:

201, electronic equipment obtains the person of telling and tells content.

202, electronic equipment determines the current data type for telling content, and data type includes at least text type, audio Any one in type, picture type and video type.

203, when current data type is text type, electronic equipment is by several text content analysis models to telling It states content to be analyzed, obtains that each text content analysis model is corresponding to tell characterize data.

In the embodiment of the present invention, text content analysis model can be the neural network model based on machine learning, can be with The content of telling of one or more text types of user's input is analyzed, to obtain each text content analysis model pair That answers tells characterize data, and the characterize data of telling that a text content analysis model analysis obtains can be one or more It is a.

204, when current data type is any one in audio types, picture type and video type, electronics Equipment is analyzed by several content analysis models content is told, and is obtained the corresponding text of each content analysis model and is told Characterize data is stated, and characterize data is told to text by several text content analysis models and is analyzed, obtains each text This content analysis model is corresponding to tell characterize data.

In the embodiment of the present invention, content analysis model can be the neural network model based on machine learning, can be to text This type, audio types, the content of telling of picture type and video type are analyzed, and tell content point to text type The model of analysis can be text content analysis model, and content analysis model is to audio types, picture type and video type Characterize data can be told for text type by telling the result that content analysis obtains, and then can pass through text content analysis mould Type analyzes the characterize data of pouring out of obtained text type, obtains final pouring out characterize data.

For example, when content analysis model is any neural network model, it is defeated that electronic equipment can will tell content Enter into neural network model, obtains corresponding text using the neural network model and tell characterize data.

Optionally, it can be marked in advance to pouring out characterize data, it such as can be to the content mark poured out in characterize data Topic text tells content body text, tells content tab text, telling content user essential information and be marked, by interior Hold analysis model to pour out characterize data analyze can directly obtain tell content the inside be marked in advance tell table Levy data.

In the embodiment of the present invention, the quantity of content analysis model can wrap for one or more, content analysis model Theme containing the nerual network technique building based on machine learning generates model, summarization generation model, label and generates model, tells It is intended to generate model, context events analysis model, the acquisition of disease content acquisition model, personage and character relation analysis model, tells State style analysis model, metaphor analysis model, story fable analysis model, Religion content analysis model, social information's analysis, section Content analysis model and psychological analysis model etc. are learned, without limitation to this embodiment of the present invention.In addition, content analysis model It can also be analyzed by traditional NLP semantic analysis technology content is told.

Wherein, content analysis model can be the neural network model based on machine learning, will tell content and is input to mind Through telling characterize data using model acquisition is corresponding in network model.Alternatively, content analysis model can also directly obtain Tell be marked inside content in advance tell characterize data, wherein the characterize data of telling being marked in advance includes telling State content title text, tell content body text, tell content tab text, tell it is any in content user essential information It is a kind of.

For example, the nerual network technique for constructing summarization generation model can be with are as follows: (1) supervised learning (Supervised Learning) training pattern: available several body text samples for telling content and corresponding abstract sample, input More round interative computations are carried out into the neural network model constructed in advance, training obtains an optimal models, abstract as Generate model.(2) semi-supervised learning (Semi-supervised Learning) training pattern: available several to tell content Body text sample and corresponding abstract sample (flag data marked each other to), and the abstract without corresponding relationship Sample or the body text sample (data untagged) for telling content without corresponding relationship, are input to the neural network constructed in advance Carry out more round interative computations in model, which can use the body text sample for telling content and right therewith The learner that the abstract sample interative computation answered obtains is that data untagged generates corresponding pseudo-text text mark or pseudo- abstract Then flag data is obtained one most to successive ignition operation, training is carried out after mixing to pseudo- flag data by text mark Excellent model, summarization generation model as.(3) Active Learning (Active Learning): available several unmarked samples Data (such as body text sample for telling content without corresponding relationship, or the summary texts sample without corresponding relationship), learner Some unmarked samples can voluntarily be picked out and inquire the label that external knowledge system obtains these samples, then again by these There is label example as training example to carry out conventional supervised learning.(4) unsupervised learning (Unsupervised Learning) Training pattern: available several unmarked sample data (are such as told body text sample without corresponding relationship, or are closed without corresponding The summary texts sample of system or the samples of text of other knowledge contents), sample data is input to pre-established neural network mould Type carries out more wheel iteration operations, trains an optimal example rule model, when the text input of content will be told to model, The model can be automatically according to example law generation summary texts.(5) model based on pre-training model: it is being based on large-scale corpus Or the pre-training model that ultra-large corpus training obtains, the mould being finely adjusted on the basis of the model according to abstract task Type.For example, can be in Google BERT (Bidirectional Encoder Representations from Transformers secondary model building) is carried out on the basis of pre-training model, and successive ignition operation, training are carried out to training corpus Obtain an optimal models, summarization generation model as.(6) what other neural network models based on machine learning constructed plucks Generate model.As it can be seen that the embodiment of the present invention can construct summarization generation model by a variety of nerual network techniques, to guarantee The accuracy of summarization generation model construction.These building modes based on neural network model are also applied for building other content Analysis model.

In the embodiment of the present invention, step 202~step 204 is executed, it can be by each content analysis model to getting The content of telling of the person of telling analyzed, so that obtaining this tells that content is corresponding to tell characterize data, so that according to telling It is more accurate that content analysis obtained tells characterize data.

205, electronic equipment determines each data type for telling characterize data.

In the embodiment of the present invention, tell characterize data data type can be the theme type, tag types, tell intention Type, psychological analysis type and essential information type etc., in this regard, the embodiment of the present invention is without limitation.

206, electronic equipment is given birth to from the matched reply of data type for determining and telling characterize data in generation mould group is replied At model, wherein a data type for telling characterize data matches a reply and generates model.

In the embodiment of the present invention, replys and may include one or more reply generation models in generation mould group, and is any one A reply, which generates model, can correspond to the data type for telling characterize data, i.e., one is told the data type of characterize data It can only match and generate model with a reply.

207, electronic equipment will be told characterize data input and be generated with the matched reply of data type for telling characterize data In model.

208, electronic equipment, which replys to generate to obtain in model from several, tells the corresponding reply content of content, reply content Characterize data is told with any one to match.

In the embodiment of the present invention, step 205~step 208 is executed, can determine each data class for telling characterize data Type, and model is generated according to the corresponding reply of data type and generates reply content so that the reply content generated and any one Tell characterize data matching, i.e., any one tells characterize data can find matching content in reply content, To improve the comprehensive of reply content.

In the method depicted in fig. 2, reply content can be made more to be bonded with the content of telling that user inputs, both promoted To telling the efficiency of content reply, and improve the usage experience of user.In addition, implementing method described in Fig. 2, ensure that The accuracy of summarization generation model construction.In addition, implementing method described in Fig. 2, basis can be made to tell content analysis and obtained To tell characterize data more accurate.In addition, implementing method described in Fig. 2, the comprehensive of reply content is improved.

Embodiment three

Referring to Fig. 3, Fig. 3 is the process signal of the generation method of another reply content disclosed by the embodiments of the present invention Figure.As shown in figure 3, the generation method of the reply content may comprise steps of:

Step 301~step 306 is identical as step 201~step 206, and the following contents repeats no more.

307, electronic equipment will be told characterize data input and be generated with the matched reply of data type for telling characterize data In model.

As an alternative embodiment, the data that electronic equipment will tell characterize data input with tell characterize data The mode that the reply of type matching generates in model may include following steps:

Electronic equipment is by each matched reply of data type told characterize data and input and tell respectively characterize data It generates in model.

Wherein, implement this embodiment, each that can be will acquire tell characterize data be input to it is corresponding Reply generate model in, with obtain it is each tell the corresponding reply content of characterize data so that obtained reply content with tell The content that the person of stating tells more matches.

As an alternative embodiment, the data that electronic equipment will tell characterize data input with tell characterize data The mode that the reply of type matching generates in model can also comprise the steps of:

Electronic equipment will tell characterize data and will be combined according to preset data built-up pattern, obtain several targets and tell table Data are levied, and obtain the target data type that each target tells characterize data；

It is matched that target is told the target data type that characterize data input tells characterize data with target by electronic equipment It replys and generates in model.

Wherein, implement this embodiment, the matched characterize data of telling of data type can be combined, if obtaining A dry target tells characterize data, and then several are told characterize data and is inputted in corresponding reply generation model respectively, with It obtains each target and tells the corresponding reply content of characterize data, simplify the mistake replied and generate the reply content that model generates Journey improves the efficiency of reply content generation.

In the embodiment of the present invention, preset data built-up pattern be may include: (1) enhanced combination: one or more to tell Characterize data can be enhanced another or it is a variety of tell characterize data characterization expression (such as: tell context events for one The preference for increasing age-sex tells characterize data, and the common sense sex determination of context events can be enhanced, beat in an adult In the event of party, the characterize data for being 10 years old into party's age is combined, so that it may obtain the event and be likely to be cruel child Furthermore the information representation of event is also possible to a plurality of characterize data and is mutually reinforcing expression)；(2) supplement improves type combination: individually telling The information for stating characterize data is imperfect, need it is multiple tell characterize data and combine could express complete characterization information. (3) similar combination: allowing certain same type to tell characterize data simple combination together, is disposably input to generation model.(4) it closes And repeat: judging whether two characterization contents for telling characterize data are identical, if identical, characterization number can be told by two Characterize data is told according to merging into one.

It is carried out as an alternative embodiment, electronic equipment will tell characterize data according to preset data built-up pattern Combination, obtains several targets and tells characterize data, and obtain the mode that each target tells the target data type of characterize data It may comprise steps of:

Electronic equipment is told each characterize data and is grouped according to data type, and it is different to obtain several groups data type Tell characterize data group；

The identical characterize data of telling of content that same group is told in characterize data group is marked electronic equipment, inside Hold identical tell in characterize data only to retain one and tell characterize data, and the characterize data of telling not retained is deleted, with It is all unique for so that any one is told the content for telling characterize data in characterize data group；

Each characterize data of telling told in characterize data group is combined by electronic equipment respectively, is obtained, will be told Characterize data is combined according to preset data built-up pattern, is obtained several targets and is told characterize data；

Electronic equipment determines that each target tells the target data type of characterize data.

Wherein, implement this embodiment, obtained several targets are told into characterize data group and are compared two-by-two, eliminate Duplicate target tells characterize data group, so that guaranteeing that obtained target is told in characterize data is not in duplicate content, The resource utilization of electronic equipment can also be improved.

308, electronic equipment replys in model from several and obtains several revertant contents, wherein in a revertant Hold from a reply model.

309, electronic equipment integrates several revertant contents, obtains telling the corresponding reply content of content, returns Multiple content is told characterize data with any one and is matched.

In the embodiment of the present invention, step 308~step 309 is executed, the content that each reply model generates can be determined For revertant content, and then revertant content is integrated, final reply content is obtained, to ensure that reply content packet Containing the revertant content that each reply model generates, so ensure that reply content with tell content and more match.

As an alternative embodiment, electronic equipment integrates several revertant contents, obtain telling interior The mode for holding corresponding reply content may include following steps:

Electronic equipment detects the quantity for telling the corresponding revertant content of content；

When the quantity for detecting revertant content is multiple, electronic equipment is according to default composite module of replying by each time Multiple sub- content is combined, and obtains telling the corresponding reply content of content；

When the quantity for detecting revertant content is one, electronic equipment is determined as revertant content to tell content pair The reply content answered.

Wherein, implement this embodiment, can be determined according to the quantity for replying the revertant content that model generates final Reply content generating mode, if the quantity of revertant content only one when, can directly by the revertant content make For reply content output, if when the quantity more than one of revertant content, it can be directly by all revertant contents into group It closes, the final reply content after being combined, and exports final reply content, to improve the effect for generating reply content Rate.

Wherein, presetting reply composite module may include revertant content comparison submodule, to same time when combining Two revertant contents that multiple model generates carry out content similarity comparison and only retain if two revertant contents are similar One revertant content, because the revertant content is similar to another revertant content given up, the revertant content Can also characterize data of telling corresponding with another revertant content match.Default composite module of replying can also include group Optimization module is closed, to multiple revertant content carry out sequence sequences when combining, increases sentence paragraph between multiple revertant contents Connection, the transition of sentence paragraph, modification word or sentence, for improving the fluency and readability of reply content.

As an alternative embodiment, electronic equipment according to it is default reply composite module by each revertant content into Row combination, the mode for obtaining telling the corresponding reply content of content may include following steps:

Electronic equipment replys composite module and is combined each revertant content according to default, initially replied in Hold；

Electronic equipment optimizes operation to initial reply content, obtain it is final tell the corresponding reply content of content, Wherein, optimization operation includes at least grammatically wrong sentence amendment operation and/or replacement/deletion sensitive word operation.

It wherein, can also include placeholder processing (such as UNK placeholder is replaced), language to the optimization operation of initial reply content Adopted entity replaces (the general pronoun " she " in initial reply content is such as replaced with to the title " small beautiful " told in content), implements This embodiment can optimize the reply content of output, with reduce grammatically wrong sentence present in reply content, sensitive word with And the problems such as wrong word, and the readability of reply content can also be optimized, to improve the usage experience for the person of telling.

In the method depicted in fig. 3, reply content can be made more to be bonded with the content of telling that user inputs, both promoted To telling the efficiency of content reply, and improve the usage experience of user.In addition, method described in implementing Fig. 3, can make Obtained reply content is more matched with the content that the person of telling tells.In addition, method described in implementing Fig. 3, improves reply The efficiency that content generates.In addition, method described in implementing Fig. 3, can be improved the memory headroom of electronic equipment.In addition, implementing Method described in Fig. 3, ensure that reply content with tell content and more match.In addition, method described in implementing Fig. 3, mentions The high efficiency for generating reply content.In addition, method described in implementing Fig. 3, improves the usage experience for the person of telling.

Example IV

The reply being related in one~embodiment of the embodiment of the present invention three, which generates model, can be the mind based on machine learning Through network model, and different types of data tells characterize data and can generate model by different replies and generate corresponding time Multiple sub- content.For example, body type tells characterize data and can be replied by the corresponding text of body type and generate model and obtain Obtain revertant content；The characterize data of telling of topic Types can be by the corresponding title reply generation model acquisition of topic Types Revertant content；Writing abstract tells characterize data and can be replied by the corresponding abstract of writing abstract and generate model and be returned Multiple sub- content；Tell intention type tell characterize data can by tell intention type it is corresponding tell intention reply generate Model obtains revertant content；The characterize data of telling of disease information type can be by the corresponding disease letter of disease information type Breath, which is replied, generates model acquisition revertant content；People information and character relation information type are told characterize data and can be passed through People information and the corresponding people information of character relation information type and character relation information-reply generate model and obtain revertant Content；The characterize data of telling of psychological analysis type can be by the corresponding psychological analysis reply life of psychological analysis type Revertant content is obtained at model；The characterize data of telling for telling style information type can be by telling style information type pair The style information of telling answered replys generation model acquisition revertant content；Metaphor information type is told characterize data and can be passed through The corresponding metaphor information-reply of metaphor information type generates model and obtains revertant content；Story fable information type tells table Model acquisition revertant content can be generated by the corresponding story fable information-reply of story fable information type by levying data；Ancestor Religion information type tells characterize data and can generate model by the corresponding religion information-reply of religion information type and be returned Multiple sub- content；The characterize data of telling of social information's type can be by the corresponding social information's reply generation of social information's type Model obtains revertant content；The characterize data of telling of scientific information type can be by the corresponding science letter of scientific information type Breath, which is replied, generates model acquisition revertant content；The characterize data of telling of semantic analysis type can be by semantic analysis type pair The semantic analysis answered, which is replied, generates model acquisition revertant content；Personal information type tells characterize data and can pass through individual The corresponding personal information of information type, which is replied, generates model acquisition revertant content.Wherein, text, which is replied, generates model, makes a summary back Repetitive generation model can be the same reply and generate model.

Wherein, implement this embodiment, the characterize data of telling of part only can be generated into revertant content, and do not have to All obtained characterize datas of telling all are generated revertant content.

As an alternative embodiment, telling characterize data by described and carrying out group according to preset data built-up pattern It closes, obtains several targets and tell characterize data, and obtain the target data type that each target tells characterize data；By the mesh Mark is told the matched reply of target data type that characterize data input tells characterize data with the target and is generated in model, no Target with target data type tells characterize data and can generate model by different replies and generates in corresponding revertant Hold.

Optionally, construct it is above-mentioned it is various reply generate the nerual network techniques of models can be with are as follows: (1) supervised learning training mould Type: available several samples for telling characterize data and corresponding revertant content sample are input to and construct in advance More round interative computations are carried out in neural network model, training obtains an optimal models, and reply as generates model.(2) Semi-supervised learning training pattern: available several samples for telling characterize data and corresponding revertant content sample (flag data marked each other to), and the revertant content sample without corresponding relationship or characterization number is told without corresponding relationship According to sample (data untagged), be input in the neural network model constructed in advance and carry out more round interative computations, the nerve Network model can use the sample for telling characterize data and corresponding revertant content sample interative computation obtain Practising device is that data untagged generates corresponding pseudo- characterize data or pseudo- revertant content, then by flag data to pseudo- data pair Successive ignition operation is carried out after mixing, training obtains an optimal models, and reply as generates model.(3) Active Learning: can To obtain several unmarked sample data (such as sample for telling characterize data without corresponding relationship, or the reply without corresponding relationship Sub- content sample), learner, which can voluntarily pick out some unmarked samples and inquire external knowledge system, obtains these samples Label, these are then had into label example as trained example to carry out conventional supervised learning again.(4) unsupervised learning training Model: available several unmarked sample data (such as telling in sample, or the revertant without corresponding relationship without corresponding relationship Hold sample or the samples of text of other knowledge contents), sample data is input to pre-established neural network model and carries out more wheels Iteration operation, trains an optimal example rule model, when will tell the text input of characterize data to model, the model It can be automatically according to example law generation revertant content.(5) it the model based on pre-training model: is being based on large-scale corpus or is surpassing The pre-training model that large-scale corpus training obtains, the model being finely adjusted on the basis of the model according to abstract task. For example, secondary model building can be carried out on the basis of Google BERT pre-training model, successive ignition is carried out to training corpus Operation, training obtain an optimal models, and reply as generates model.(6) other neural network moulds based on machine learning The reply of type building generates model.As it can be seen that the embodiment of the present invention, which can construct to reply by a variety of nerual network techniques, generates mould Type, to ensure that the accuracy replied and generate model construction.

For example, the context events type that electronic equipment is analyzed tell characterize data can for " I this time Graduation examination does not pass through, and university does not finish industry, I feels life no future, I am a little desperate ", it can be by the situation The characterize data of telling of event type is input in the corresponding context events reply generation model of context events type, available Revertant content " if whether there are also make-up examination chances? diploma is not got really, that is also not the life end, the master of school Syllabus is learning knowledge, can also be with learning knowledge and technical ability outside school.Life is very long, inevitably meets with setback, everybody one Sample, your life just starts soon, to refuel ".As it can be seen that electronic equipment can pass through the data class for different data types The corresponding reply of type generates model generation and the matched reply content of current data type, so that in the reply that electronic equipment generates Hold and is more matched with the content of telling of the person's of telling input.

Embodiment five

Referring to Fig. 4, Fig. 4 is a kind of structural schematic diagram of the generating means of reply content disclosed by the embodiments of the present invention. Wherein, as shown in figure 4, the generating means of the reply content may include:

Acquiring unit 401, for obtaining the content for the person of telling.

Analytical unit 402 obtains several and tells characterize data, this tells characterization for analyzing to telling content Data include at least and tell text data.

As an alternative embodiment, analytical unit 402 analyzes the content of telling for getting the person of telling, obtain The mode that characterize data is told to several is specifically as follows:

The content of telling for getting the person of telling is analyzed, determines the data type for telling content；

When detecting the data type for telling content is text type, pass through the corresponding content analysis model of text type The content of telling of text type is analyzed, several is obtained and tells characterize data；

When detecting the data type for telling content is picture type, by graph image identification model to picture type Tell content carry out discriminance analysis, the available text information told in content picture, and by text type it is corresponding Content analysis model analyzes text information, obtains several and tells characterize data, can also identify picture type When telling in content comprising facial image, the identification based on human facial expression recognition model to facial image obtains the person's of telling Psychology characterize data, and can will tell text data and psychology characterize data be determined as jointly identification obtain it is several It is a to tell characterize data；

When detecting the data type for telling content is audio types, by the speech recognition module based on ASR to telling It states content to be identified, obtains telling the corresponding text information of content voice, and then pass through the corresponding content analysis of text type Model analyzes text information, obtains several and tells characterize data, is also based on sound Emotion identification model to telling The person's of stating sound of speaking feature carries out discriminance analysis, obtains the psychology characterize data for the person of telling；

When detecting the data type for telling content is video type, by the speech recognition module based on ASR to telling The audio-frequency information stated in content is identified, the text information for telling content is obtained, can also be by being based on limbs Activity recognition Analysis model carries out the analysis of subconsciousness Activity recognition to the limbs behavior of personage, obtains the psychology characterize data for the person of telling, with And all text informations are analyzed by the corresponding content analysis model of text type, it obtains several and tells characterization number According to.

Generation unit 403, several for being obtained using analytical unit 402 are told characterize data as foundation, pass through reply It generates the generation of mould group and tells the corresponding reply content of content, which tells characterize data with any one and match.

As it can be seen that implementing the generating means of reply content described in Fig. 4, reply content can be made to tell with what user inputted Content is more bonded, and has not only been improved to the efficiency for telling content reply, but also has been improved the usage experience of user.In addition, implementing Fig. 4 Described device improves the accuracy that analysis obtains telling characterize data.In addition, implementing device described in Fig. 4, improve Diversity of the electronic equipment for the analysis mode of the content of image type.

Embodiment six

Referring to Fig. 5, Fig. 5 is the structural representation of the generating means of another reply content disclosed by the embodiments of the present invention Figure.Wherein, the generating means of reply content shown in fig. 5 are that the generating means of reply content as shown in Figure 4 optimize It arrives.The analytical unit 402 of the generating means of reply content shown in fig. 5 may include:

First determines subelement 4021, and for determining the current data type for telling content, data type includes at least text Any one in this type, audio types, picture type and video type.

First analysis subelement 4022, the current data type for determining when the first determining subelement 4021 is text class It when type, is analyzed by several text content analysis models content is told, obtains each text content analysis model pair That answers tells characterize data.

Second analysis subelement 4023, the current data type for determining when the first determining subelement 4021 is audio class When any one in type, picture type and video type, divided by several content analysis models content is told Analysis obtains the corresponding text of each content analysis model and tells characterize data, and passes through several text content analysis models pair Text is told characterize data and is analyzed, and obtains that each text content analysis model is corresponding to tell characterize data.

In the embodiment of the present invention, it can be carried out by tell content of each content analysis model to the person of telling got Analysis, so that obtaining this tells that content is corresponding to tell characterize data, so as to tell characterization according to tell that content analysis obtains Data are more accurate.

As an alternative embodiment, the generation unit 403 of the generating means of reply content shown in fig. 5 can wrap It includes:

Second determines subelement 4031, for determining each data type for telling characterize data；

Third determines subelement 4032, for from replying, determining in generation mould group and second to determine what subelement 4031 determined The matched reply of data type for telling characterize data generates model, wherein one is told the data type matching of characterize data One reply generates model；

Input subelement 4033, for will tell characterize data input with third determine subelement 4032 determine tell table The matched reply of data type for levying data generates in model；

Subelement 4034 is obtained, for inputting several reply generation moulds for telling characterize data from input subelement 4033 It is obtained in type and tells the corresponding reply content of content.

Wherein, implement this embodiment, can determine each data type for telling characterize data, and according to data class The corresponding reply of type generates model and generates reply content, so that the reply content generated tells characterize data with any one Match, i.e., any one tells characterize data can find matching content in reply content, to improve reply Content it is comprehensive.

As it can be seen that implementing the generating means of reply content described in Fig. 5, reply content can be made to tell with what user inputted Content is more bonded, and has not only been improved to the efficiency for telling content reply, but also has been improved the usage experience of user.In addition, implementing Fig. 5 Described device ensure that the accuracy replied and generate model construction.In addition, implementing device described in Fig. 5, root can be made According to tell that content analysis obtains to tell characterize data more accurate.In addition, implementing device described in Fig. 5, reply is improved Content it is comprehensive.

Embodiment seven

Referring to Fig. 6, Fig. 6 is the structural representation of the generating means of another reply content disclosed by the embodiments of the present invention Figure.Wherein, the generating means of reply content shown in fig. 6 are that the generating means of reply content as shown in Figure 5 optimize It arrives.The input subelement 4033 of the generating means of reply content shown in fig. 6 may include:

First input module 40331, for by each data told characterize data and input and tell respectively characterize data The reply of type matching generates in model.Alternatively,

Composite module 40332 is combined according to preset data built-up pattern for that will tell characterize data, obtains several Target tells characterize data, and obtains the target data type that each target tells characterize data.

Second input module 40333, the target for obtaining composite module 40332 tell characterize data input and target The matched reply of target data type for telling characterize data generates in model.

In the embodiment of the present invention, each that can be will acquire tells characterize data and is input to corresponding reply life At in model, with obtain it is each tell the corresponding reply content of characterize data so that obtained reply content is told with the person of telling Content more match.Further, it is also possible to which the matched characterize data of telling of data type is combined, several targets are obtained Characterize data is told, and then several are told into characterize data and is inputted in corresponding reply generation model respectively, it is each to obtain Target tells the corresponding reply content of characterize data, simplifies the process replied and generate the reply content that model generates, improves The efficiency that reply content generates.

As an alternative embodiment, composite module 40332 will tell characterize data according to preset data combination die Type is combined, and is obtained several targets and is told characterize data, and obtains the target data type that each target tells characterize data Mode be specifically as follows:

Tell characterize data by each and be grouped according to data type, obtain several groups data type it is different tell table Levy data group；

The identical characterize data of telling of content that same group is told in characterize data group is marked, it is identical in content It tells a reservation one in characterize data and tells characterize data, and the characterize data of telling not retained is deleted, so that any one A content for telling characterize data told in characterize data group is all unique；

Each characterize data of telling told in characterize data group is combined respectively, obtains, characterize data will be told It is combined according to preset data built-up pattern, obtains several targets and tell characterize data；

Determine that each target tells the target data type of characterize data.

Wherein, implement this embodiment, can data type is identical and content also identical characterize data of telling is deleted It removes, so that each tell telling characterize data and will not all repeat in characterize data group, to guarantee to tell characterization according to each It is not in duplicate content that the target that data groups obtain, which is told in characterize data, can also improve the memory of electronic equipment Space.

As an alternative embodiment, the acquisition subelement 4034 of the generating means of reply content shown in fig. 6 can To include:

Module 40341 is obtained, obtains several revertant contents for replying in model from several, wherein one is returned Multiple sub- content is from a reply model；

Module 40342 is integrated, integrates, obtains for several revertant contents that module 40341 obtains will to be obtained Tell the corresponding reply content of content.

Wherein, implement this embodiment, the content that each reply model generates can be determined as revertant content, into And integrate revertant content, final reply content is obtained, to ensure that reply content includes each reply model The revertant content of generation, so ensure that reply content with tell content and more match.

As an alternative embodiment, the integration module 40342 of the generating means of reply content shown in fig. 6 can be with Include:

Detection sub-module 403421, for detecting the quantity for telling the corresponding revertant content of content；

Submodule 403422 is combined, for detecting that the quantity of revertant content is multiple when detection sub-module 403421 When, each revertant content is combined according to default reply composite module, obtains telling the corresponding reply content of content；

Submodule 403423 is determined, for detecting that the quantity of revertant content is one when detection sub-module 403421 When, revertant content is determined as to tell the corresponding reply content of content.

As an alternative embodiment, the combination submodule 403422 of the generating means of reply content shown in fig. 6 May include:

Combine component 4034221, when for detecting that the quantity of revertant content is multiple when detection sub-module 403421, Each revertant content is combined according to default reply composite module, obtains initial reply content；

Optimization component 4034222, for optimizing operation to the initial reply content that combine component 4034221 obtains, Obtain final telling the corresponding reply content of content, wherein optimization operation include at least grammatically wrong sentence amendment operation and/or replacement/ Delete sensitive word operation.

As it can be seen that implementing the generating means of reply content described in Fig. 6, reply content can be made to tell with what user inputted Content is more bonded, and has not only been improved to the efficiency for telling content reply, but also has been improved the usage experience of user.In addition, implementing Fig. 6 Described device, the reply content that can make more are matched with the content that the person of telling tells.It is retouched in addition, implementing Fig. 6 The device stated improves the efficiency of reply content generation.In addition, implementing device described in Fig. 6, electronic equipment can be improved Memory headroom.In addition, implement device described in Fig. 6, ensure that reply content with tell content and more match.In addition, implementing Device described in Fig. 6 improves the efficiency for generating reply content.In addition, implementing device described in Fig. 6, the person of telling is improved Usage experience.

Embodiment eight

Referring to Fig. 7, Fig. 7 is the structural schematic diagram of a kind of electronic equipment disclosed by the embodiments of the present invention.As shown in fig. 7, The electronic equipment may include:

It is stored with the memory 701 of executable program code；

The processor 702 coupled with memory 701；

Wherein, processor 702 calls the executable program code stored in memory 701, executes the above each method and implements Some or all of method in example step.

A kind of computer readable storage medium is also disclosed in the embodiment of the present invention, wherein computer-readable recording medium storage Program code, wherein program code includes for executing some or all of the method in above each method embodiment step Instruction.

A kind of computer program product is also disclosed in the embodiment of the present invention, wherein when computer program product on computers When operation, so that computer executes some or all of the method in such as above each method embodiment step.

The embodiment of the present invention is also disclosed a kind of using distribution platform, wherein using distribution platform for issuing computer journey Sequence product, wherein when computer program product is run on computers, so that computer executes such as the above each method embodiment In some or all of method step.

It should be understood that " embodiment of the present invention " that specification is mentioned in the whole text mean special characteristic related with embodiment, Structure or characteristic is included at least one embodiment of the present invention.Therefore, the whole instruction occur everywhere " in the present invention In embodiment " not necessarily refer to identical embodiment.In addition, these a particular feature, structure, or characteristics can be with any suitable Mode combines in one or more embodiments.Those skilled in the art should also know that embodiment described in this description Alternative embodiment is belonged to, related actions and modules are not necessarily necessary for the present invention.

In various embodiments of the present invention, it should be appreciated that magnitude of the sequence numbers of the above procedures are not meant to execute suitable Successively, the execution sequence of each process should be determined by its function and internal logic the certainty of sequence, without coping with the embodiment of the present invention Implementation process constitutes any restriction.

In addition, the terms " system " and " network " are often used interchangeably herein.It should be understood that the terms "and/or", only a kind of incidence relation for describing affiliated partner, indicates may exist three kinds of relationships, such as A and/or B, can To indicate: individualism A exists simultaneously A and B, these three situations of individualism B.In addition, character "/" herein, typicallys represent Forward-backward correlation object is a kind of relationship of "or".

In embodiment provided by the present invention, it should be appreciated that " B corresponding with A " indicates that B is associated with A, can be with according to A Determine B.It is also to be understood that determine that B is not meant to determine B only according to A according to A, it can also be according to A and/or other information Determine B.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage Medium include read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), programmable read only memory (Programmable Read-only Memory, PROM), erasable programmable is read-only deposits Reservoir (Erasable Programmable Read Only Memory, EPROM), disposable programmable read-only memory (One- Time Programmable Read-Only Memory, OTPROM), the electronics formula of erasing can make carbon copies read-only memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM) or other disc memories, magnetic disk storage, magnetic tape storage or can For carrying or any other computer-readable medium of storing data.

Above-mentioned unit as illustrated by the separation member may or may not be physically separated, aobvious as unit The component shown may or may not be physical unit, can be in one place, or may be distributed over multiple nets On network unit.Some or all of units can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.

In addition, each functional unit in various embodiments of the present invention can integrate in one processing unit, it is also possible to Each unit physically exists alone, and can also be integrated in one unit with two or more units.Above-mentioned integrated unit Both it can take the form of hardware realization, can also realize in the form of software functional units.

If above-mentioned integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product, It can store in a retrievable memory of computer.Based on this understanding, technical solution of the present invention substantially or Person says all or part of of the part that contributes to existing technology or the technical solution, can be in the form of software products It embodies, which is stored in a memory, including several requests are with so that a computer is set Standby (can be personal computer, server or network equipment etc., specifically can be the processor in computer equipment) executes Some or all of each embodiment above method of the invention step.

The generation method and device of a kind of reply content disclosed by the embodiments of the present invention are described in detail above, this Apply that a specific example illustrates the principle and implementation of the invention in text, the explanation of above example is only intended to It facilitates the understanding of the method and its core concept of the invention；At the same time, for those skilled in the art, think of according to the present invention Think, there will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not be construed as pair Limitation of the invention.

Claims

1. a kind of generation method of reply content, which is characterized in that the described method includes:

Obtain the person of telling tells content；

The content of telling is analyzed, several is obtained and tells characterize data, it is described tell characterize data and include at least tell State text data；

Characterize data is told as foundation using described several, tells the corresponding reply of content described in the generation of mould group by replying to generate Content, the reply content are matched with any one of characterize data of telling.

2. being obtained several the method according to claim 1, wherein described analyze the content of telling It is a to tell characterize data, comprising:

The current data type of content is told described in determination, the data type includes at least text type, audio types, picture Any one in type and video type；

When the current data type is the text type, by several text content analysis models in described tell Appearance is analyzed, and obtains that each text content analysis model is corresponding to tell characterize data；

When the current data type is any one in the audio types, the picture type and the video type When, the content of telling is analyzed by several content analysis models, it is corresponding to obtain each content analysis model Text tell characterize data, and characterize data is told to the text by several text content analysis models and is divided Analysis, obtains that each text content analysis model is corresponding to tell characterize data.

3. method according to claim 1 or 2, which is characterized in that it is described with described several tell characterize data be according to According to by telling the corresponding reply content of content described in reply generation mould group generation, comprising:

Determine each data type for telling characterize data；

The determining and described matched reply generation model of data type for telling characterize data in mould group is generated from replying, wherein The data type that characterize data is told described in one matches the reply and generates model；

Characterize data input and the matched reply generation model of the data type for telling characterize data are told by described In；

4. according to the method described in claim 3, it is characterized in that, described tell described characterize data input and tell with described The matched reply of the data type of characterize data generates in model, comprising:

Each characterize data of telling is inputted and the matched reply of data type for telling characterize data respectively It generates in model；Alternatively,

It tells characterize data by described and is combined according to preset data built-up pattern, obtain several targets and tell characterize data, And obtain the target data type that each target tells characterize data；

The target is told into characterize data input and the target tells the matched reply of target data type of characterize data It generates in model.

5. according to the method described in claim 4, it is characterized in that, described generate in model from reply described in several obtains institute Statement states the corresponding reply content of content, comprising:

Several revertant contents are obtained from several described described reply models, wherein a revertant content is come From in a reply model；

6. according to the method described in claim 5, it is characterized in that, described integrate several described revertant contents, The corresponding reply content of content is told described in obtaining, comprising:

When the quantity for detecting the revertant content is multiple, according to default reply built-up pattern by each revertant Content is combined, and obtains described telling the corresponding reply content of content；

When the quantity for detecting the revertant content is one, the revertant content is determined as described to tell content pair The reply content answered.

7. according to the method described in claim 6, it is characterized in that, the default built-up pattern of replying of the basis is by each described time Multiple sub- content is combined, and obtains described telling the corresponding reply content of content, comprising:

Operation is optimized to the initial reply content, obtains final described telling the corresponding reply content of content, wherein The optimization operation includes at least grammatically wrong sentence amendment operation and/or replacement/deletion sensitive word operation.

8. a kind of generating means of reply content characterized by comprising

Acquiring unit tells content for obtain the person of telling；

Analytical unit obtains several and tells characterize data for analyzing the content of telling, described to tell characterization number Text data is told according to including at least；

Generation unit is told described in the generation of mould group for telling characterize data as foundation using described several by replying to generate The corresponding reply content of content, the reply content are matched with any one of characterize data of telling.

9. the generating means of reply content according to claim 8, which is characterized in that the analytical unit includes:

First determines subelement, and for telling the current data type of content described in determination, the data type includes at least text Any one in this type, audio types, picture type and video type；

First analysis subelement, for passing through several content of text when the current data type is the text type Analysis model analyzes the content of telling, and obtains that each text content analysis model is corresponding to tell characterization number According to；

Second analysis subelement, for being the audio types, the picture type and described when the current data type When any one in video type, the content of telling is analyzed by several content analysis models, is obtained each The corresponding text of the content analysis model tells characterize data, and by several text content analysis models to the text It tells characterize data to be analyzed, obtains that each text content analysis model is corresponding to tell characterize data.

10. the generating means of reply content according to claim 8 or claim 9, which is characterized in that the generation unit includes:

Third determines subelement, for matched with the data type for telling characterize data from determination in generation mould group is replied It replys and generates model, wherein the data type for telling characterize data described in one matches the reply and generates model；

Subelement is inputted, for telling characterize data input and the matched institute of data type for telling characterize data for described Reply is stated to generate in model；

Subelement is obtained, described tells the corresponding reply content of content for generating to obtain in model from reply described in several.

11. the generating means of reply content according to claim 10, which is characterized in that the input subelement includes:

First input module, for inputting and the data class for telling characterize data each characterize data of telling respectively The matched reply of type generates in model；Alternatively,

Composite module obtains several targets for telling characterize data by described and being combined according to preset data built-up pattern Characterize data is told, and obtains the target data type that each target tells characterize data；

Second input module tells the number of targets of characterize data with the target for the target to be told characterize data input It is generated in model according to the reply of type matching.

12. the generating means of reply content according to claim 11, which is characterized in that the acquisition subelement includes:

Module is obtained, for obtaining several revertant contents from several described described reply models, wherein described in one Revertant content is from a reply model；

Module is integrated, for integrating several described revertant contents, obtains described tell in the corresponding reply of content Hold.

13. the generating means of reply content according to claim 12, which is characterized in that the module of integrating includes:

Combine submodule, for when detect the revertant content quantity be it is multiple when, according to default reply built-up pattern Each revertant content is combined, obtains described telling the corresponding reply content of content；

Submodule is determined, for when the quantity for detecting the revertant content is one, the revertant content to be determined The corresponding reply content of content is told to be described.

14. the generating means of reply content according to claim 13, which is characterized in that the combination submodule includes:

Combine component, for being incited somebody to action according to default reply built-up pattern when the quantity for detecting the revertant content is multiple Each revertant content is combined, and obtains initial reply content；

Optimization component obtains that final described to tell content corresponding for optimizing operation to the initial reply content Reply content, wherein the optimization operation includes at least grammatically wrong sentence amendment operation and/or replacement/deletion sensitive word operation.