CN108806671A - Semantic analysis, device and electronic equipment - Google Patents
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1822—Parsing for meaning understanding
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
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Abstract
The present invention provides a kind of semantic analysis, device and electronic equipments, are related to semantic analysis technology field, which includes：Receive the first voice messaging input by user；Convert the first voice messaging to text information；Text information is handled using verbal model；The semantic understanding result that semantic analysis generates text information is carried out to treated text information using the deep learning model of structure；The semantic understanding result of text information is exported.Therefore, technical solution provided by the invention, the technical issues of traditional semantic analysis existing in the prior art cannot systematically carry out complicated semantic analysis can be alleviated, propose a kind of semantic analysis of systematization, the accuracy that the semantic analysis of complicated voice input can be improved, promotes the development of human-computer interaction intelligent.
Description
Technical field
The present invention relates to semantic analysis technology fields, more particularly, to a kind of semantic analysis, device and electronic equipment.
Background technology
With the development of science and technology, intellectual technology is rapidly developed and is popularized in electronic field.Speech recognition technology is intelligence
How an important ring for energy technology, accurately identify that the voice of user is the development trend of intellectual technology.Currently, speech recognition skill
The development of art largely improves the level of human-computer interaction, and semantic analysis technology is as the crucial portion for understanding natural language
Point, the important task for how fully analyzing and understanding that the input of user's natural language is semantic is carry, therefore for the intelligence of intelligence system
Change degree has conclusive effect.However, traditional semantic analysis effect in terms of the semantic analysis that simple voice inputs
Or it is good, but do not have the semantic analysis ability of systematization for complicated voice input, it cannot meet and increasingly improve
Human-computer interaction intelligent demand, how to improve the accuracy of the semantic analysis of complicated voice input becomes human-computer interaction urgently
The technical issues of solution.
Invention content
In view of this, the purpose of the present invention is to provide semantic analysis, device and electronic equipment, to alleviate existing skill
Traditional semantic analysis present in art cannot systematically carry out the technical issues of complicated semantic analysis.
In a first aspect, an embodiment of the present invention provides a kind of semantic analysis, including：
Receive the first voice messaging input by user；
Convert first voice messaging to text information；
The text information is handled using verbal model；
Semantic analysis is carried out to treated text information using the deep learning model of structure and generates text information
Semantic understanding result；
The semantic understanding result of text information is exported.
With reference to first aspect, an embodiment of the present invention provides the first possible embodiments of first aspect, wherein institute
It states and converts first voice messaging to text information, specifically include：
Judge whether first voice messaging is received pronunciation information；
If it is not, first voice messaging is then converted to received pronunciation information；
Convert the received pronunciation information to text information.
With reference to first aspect, an embodiment of the present invention provides second of possible embodiments of first aspect, wherein institute
It states and the text information is handled using verbal model, specifically include：
Character segmentation, filtering, classification, part of speech analysis, part-of-speech tagging, extraction mark are carried out to text information using verbal model
Label, obtain multiple participle phrases.
With reference to first aspect, an embodiment of the present invention provides the third possible embodiments of first aspect, wherein institute
It states and the semantic reason that semantic analysis generates text information is carried out to treated text information using the deep learning model of structure
Solution is as a result, specifically include：
Context understanding is carried out to treated text information using the deep learning models coupling application scenarios of structure
It is disambiguated with semanteme, generates the semantic understanding result of text information.
The third possible embodiment with reference to first aspect, an embodiment of the present invention provides the 4th kind of first aspect
Possible embodiment, wherein the deep learning models coupling application scenarios using structure believe treated word
Breath carries out context understanding and semantic disambiguation, generates the semantic understanding of text information as a result, specifically including：
Multiple phrase combination contexts of treated text information are carried out up and down using the deep learning model of structure
Unity and coherence in writing solution, semantic disambiguation；Obtain the semantic results of multiple phrases；
The semantic results of multiple phrases are compared with the phrase of knowledge mapping respectively, obtain the similarity of each phrase
Value, using the highest phrase of similarity value as the semantic results of each phrase, obtains the semantic results of multiple phrases；
The semantic results of multiple phrases are combined, the semantic understanding result of text information is generated.
The third possible embodiment with reference to first aspect, an embodiment of the present invention provides the 5th kind of first aspect
Possible embodiment, wherein the deep learning models coupling application scenarios using structure believe treated word
Breath carries out context understanding and semantic disambiguation, generates the semantic understanding of text information as a result, specifically including：
Multiple phrase combination contexts of treated text information are carried out up and down using the deep learning model of structure
Unity and coherence in writing solution, semantic disambiguation；Obtain the semantic results of multiple phrases；
By the semantic results combination knowledge mapping of multiple phrases, the internal relation and/or logical relation of multiple phrases are analyzed,
Generate the semantic understanding result of text information.
With reference to first aspect, an embodiment of the present invention provides the 6th kind of possible embodiments of first aspect, wherein institute
It states and exports the semantic understanding result of text information, specifically include：
The semantic understanding result of text information is exported in the form of word；
And/or the semantic understanding result of text information is exported in the form of received pronunciation；
And/or the semantic understanding result of text information is exported in the form of picture；
And/or the semantic understanding result of text information is exported in the form of video；
And/or the semantic understanding result of text information is exported in the form of hyperlink.
With reference to first aspect, an embodiment of the present invention provides the 7th kind of possible embodiments of first aspect, wherein also
Including：
In the training process and/or application process of the deep learning model, aid mark is carried out by manual intervention,
To improve the understanding accuracy of participle.
Second aspect, the embodiment of the present invention also provide a kind of semantic analysis device, including：
Receiving module, for receiving the first voice messaging input by user；
Conversion module, for converting first voice messaging to text information；
Processing module, for being handled the text information using verbal model；
Analysis module carries out semantic analysis life for the deep learning model using structure to treated text information
At the semantic understanding result of text information；
Output module, for exporting the semantic understanding result of text information.
The third aspect, the embodiment of the present invention additionally provide a kind of electronic equipment, including memory, processor and are stored in institute
The computer program that can be run on memory and on the processor is stated, the processor executes real when the computer program
The step of showing the semantic analysis described in any one of above-mentioned first aspect and its possible embodiment.
Fourth aspect, an embodiment of the present invention provides a kind of meters for the non-volatile program code that can perform with processor
Calculation machine readable medium, said program code make the processor execute the aforementioned semantic analysis referred to.
The embodiment of the present invention brings following advantageous effect：
In semantic analysis provided in an embodiment of the present invention, device and electronic equipment, wherein the semantic analysis packet
It includes：Receive the first voice messaging input by user；Convert the first voice messaging to text information；Using verbal model to word
Information is handled；Semantic analysis is carried out to treated text information using the deep learning model of structure and generates word letter
The semantic understanding result of breath；The semantic understanding result of text information is exported.Therefore, technology provided in an embodiment of the present invention
Scheme, can alleviate traditional semantic analysis existing in the prior art cannot systematically carry out complicated semantic analysis
Technical problem, it is proposed that a kind of semantic analysis scheme of systematization can improve the standard of the semantic analysis of complicated voice input
True property promotes the development of human-computer interaction intelligent.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The purpose of the present invention and other advantages are in specification, claims
And specifically noted structure is realized and is obtained in attached drawing.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment cited below particularly, and coordinate
Appended attached drawing, is described in detail below.
Description of the drawings
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, in being described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, other drawings may also be obtained based on these drawings.
Fig. 1 is a kind of flow chart of semantic analysis provided in an embodiment of the present invention；
Fig. 2 is the flow chart of another semantic analysis provided in an embodiment of the present invention；
Fig. 3 is that an embodiment of the present invention provides a kind of schematic diagrames of semantic analysis device；
Fig. 4 is the schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, shall fall within the protection scope of the present invention.
Currently, the development of speech recognition technology largely improves the level of human-computer interaction, and semantic analysis technology is made
To understand the key component of natural language, the important task for how fully analyzing and understanding that the input of user's natural language is semantic is carry,
Therefore there is conclusive effect for the intelligence degree of intelligence system.However, traditional semantic analysis is in simple voice
Effect or good in terms of the semantic analysis of input, but do not have the semantic analysis of systematization for complicated voice input
Ability cannot meet increasing human-computer interaction intelligent demand, how improve the semantic analysis of complicated voice input
Accuracy becomes human-computer interaction technical problem urgently to be resolved hurrily, is based on this, a kind of semantic analysis side provided in an embodiment of the present invention
Method, device and electronic equipment, can alleviate traditional semantic analysis existing in the prior art cannot systematically carry out
The technical issues of complicated semantic analysis, can improve the accuracy of the semantic analysis of complicated voice input.
For ease of understanding the present embodiment, first to a kind of semantic analysis disclosed in the embodiment of the present invention into
Row is discussed in detail.
Embodiment one：
As shown in Figure 1, an embodiment of the present invention provides a kind of semantic analysis, specifically include：
Step S101：Receive the first voice messaging input by user；
Here the first voice messaging can be received pronunciation information, such as mandarin, can also be non-standard language message
Breath, such as dialect or foreign language；It can also be the voice messaging for including non-default language, such as be mingled with foreign language or dialect
Mandarin etc..
Step S102：Convert the first voice messaging to text information；
Specifically, step S102 is mainly realized by following steps：
1) judge whether first voice messaging is received pronunciation information；
Specifically, judging whether first voice messaging includes non-default language；The non-default language includes dialect
Or foreign language；
2) if it is not, first voice messaging is then converted to received pronunciation information；
The step 2) is mainly realized by following steps：
A is not if it is determined that first voice messaging is for received pronunciation information；Judge that first voice messaging includes
When non-default language, first voice messaging is extracted using dialect model, is divided, is obtained at least one non-
Preset language phrase；And record the location information that non-default language phrase is located in the first voice messaging；
Here dialect model includes dialect model or foreign language model etc.；Non-default language phrase includes dialecticism
Group or foreign language phrase；
Specifically, if it is determined that first voice messaging is not for received pronunciation information, then dialect model or foreign language are utilized
Model etc. respectively extracts first voice messaging, divides, and obtains at least one dialect phrase or foreign language phrase；And
Record dialect phrase or foreign language phrase are located at the location information in the first voice messaging；
B searches phrase and corresponds to table and disambiguated in conjunction with context progress semanteme corresponding with the non-default language phrase to obtain
Standard language phrase；
Step B specifically includes following steps：
B1 is according to position of the non-default language phrase in the voice messaging to judge the non-default language word
The part of speech of group；
B2 is based on the part of speech and searches phrase corresponding to table to obtain the meaning of a word corresponding with the non-default language phrase；
B3 judges whether the meaning of a word is multiple；
Here multiple refer to two or more.
Step B3 is specifically included：
B31 is not multiple when the meaning of a word, then directly using the meaning of a word as standard language corresponding with the non-default language phrase
Phrase；
B32 is then executed when the meaning of a word is multiple and is judged whether multiple meaning of a word are near synonym, if multiple meaning of a word are nearly justice
Word selects frequency of use highest meaning of a word output from multiple meaning of a word, using the meaning of a word as with the non-default language phrase pair
The standard language phrase answered；If multiple meaning of a word are not near synonym, semantic understanding is based on context carried out, generative semantics understand knot
Fruit；Determine that the meaning of a word of the non-default language phrase, the meaning of a word are and the non-default language based on the semantic understanding result
The corresponding standard language phrase of words group；
It should be pointed out that in another embodiment, if multiple meaning of a word are not near synonym, based on context carrying out language
Reason and good sense solution, generative semantics understand result；According to the volume of the non-default language phrase to judge emotion information；Based on institute's predicate
Reason and good sense solution result and emotion information determine the meaning of a word of the non-default language phrase, using the meaning of a word as with the non-default language
The corresponding standard language phrase of phrase；
C is located at the location information in the first voice messaging by the standard speech words according to the non-default language phrase of record
Group is back to the position in first voice messaging, generates received pronunciation information；
D converts the received pronunciation information to text information.
3) if so, i.e. described first voice messaging does not include non-default language；It then directly executes and believes first voice
Breath is converted into text information.
It should be pointed out that user can also directly input text information, at this point, after receiving the text information of user
Directly execute step S103.
Step S103：The text information is handled using verbal model；
Here processing include character segmentation, filtering, classification, part of speech analysis, part-of-speech tagging, extract label at least one
Kind.Wherein, character segmentation refers to participle to obtain multiple phrases, and filtering refers to stop words or meaningless word (such as ", ") mistake
Filter.Classification includes being divided into Subject, Predicate and Object according to syntax rule；Part of speech analysis includes notional word, participle differentiation；Verb, noun divide；Word
Property standard and according to part of speech analyze result markup information, generate label.
Specifically, carrying out character segmentation, stop words or the filtering of meaningless word to text information using verbal model, dividing
Class, part of speech analysis, part-of-speech tagging, extraction label, obtain multiple participle phrases.
Step S104：Semantic analysis is carried out to treated text information using the deep learning model of structure and generates text
The semantic understanding result of word information；
When it is implemented, step S104 includes：
(1) unity and coherence in writing up and down is carried out to treated text information using the deep learning models coupling application scenarios of structure
Solution and semantic disambiguation, generate the semantic understanding result of text information.
Specifically, the step (1) can be realized one of in the following manner：
First way：
1, multiple phrase combination contexts of treated text information are carried out using the deep learning model of structure
Hereafter understand, semantic disambiguation (polysemant)；Obtain the semantic results of multiple phrases；
What above-mentioned semantic disambiguation was carried out mainly for the phrase (including standard language phrase) with multiple meanings, with determination
The concrete meaning of the phrase with multiple meanings in text information.
2, the semantic results of multiple phrases are compared with the phrase of knowledge mapping respectively, obtain the similar of each phrase
Angle value obtains the semantic results of multiple phrases using the highest phrase of similarity value as the semantic results of each phrase；
Here knowledge mapping is the specialized vocabulary of every field and its term vector collection of illustrative plates of meaning, is with medical domain
Example, knowledge mapping can be medical speciality vocabulary and its term vector collection of illustrative plates of meaning, can also be disease vocabulary and its meaning, shadow
The factor of sound, the relevant diagnosis and treatment collection of illustrative plates for treating element.
3, the semantic results of multiple phrases are combined.
Specifically, the semantic results of multiple phrases to be returned to the original position of text information, can also utilize the standard
Language phrase is combined according to Chinese rule, obtains standard sentence；Using preset standard language model to the standard speech
Sentence is analyzed, and is understood the meaning of standard sentence, is generated the semantic understanding result of text information.
By being combined according to Chinese rule, that is, determines the Subject, Predicate and Object of text information, prevent upside-down mounting, while facilitating machine
Understanding, is established and unified criterion of identification, raising understand speed and efficiency.
The second way：
A carries out up and down multiple phrase combination contexts of treated text information using the deep learning model of structure
Unity and coherence in writing solution, semantic disambiguation；Obtain the semantic results of multiple phrases；
The semantic results combination knowledge mapping of multiple phrases is analyzed the internal relation and/or logical relation of multiple phrases by b
(causality) generates the semantic understanding result of text information.Here internal relation includes corresponding between multiple phrases
System, logical relation includes causality.
Such as the information of input is " whom son of the mother of boy A is ", the multiple phrases obtained by above-mentioned steps：Man
Child is analyzed the internal relation and logical relation of multiple phrases by knowledge mapping, is inferred
It obtains the meaning of text information and as a result, i.e. answer is " A ".
Step S105：The semantic understanding result of text information is exported.
Specifically, step S105 is mainly realized at least one of in the following manner：
Mode one exports the semantic understanding result of text information in the form of word；
Mode two exports the semantic understanding result of text information in the form of received pronunciation；
Specifically, using received pronunciation model, transfers vocal print and be converted to received pronunciation and export；
Further, can also received pronunciation be subjected to two times transfer, is converted to the output of the second voice messaging, above-mentioned the
Two voice messagings can be dialect or other language, improve user experience and cordiality degree；
Mode three exports the semantic understanding result of text information in the form of picture.
Mode four exports the semantic understanding result of text information in the form of video.
Mode five exports the semantic understanding result of text information in the form of hyperlink.
Mode six even two or more exports arbitrary two kinds in aforesaid way in combination.Such as picture and text are defeated
Go out or language and characters output etc..
Semantic analysis provided in an embodiment of the present invention, including：Receive the first voice messaging input by user；By first
Voice messaging is converted into text information；Text information is handled using verbal model；Utilize the deep learning model of structure
The semantic understanding result that semantic analysis generates text information is carried out to treated text information；By the semanteme reason of text information
Solution result is exported.Therefore, technical solution provided in an embodiment of the present invention can be alleviated existing in the prior art traditional
Semantic analysis cannot systematically carry out the technical issues of complicated semantic analysis, it is proposed that a kind of semantic analysis of systematization
Scheme can improve the accuracy of the semantic analysis of complicated voice input, promote the development of human-computer interaction intelligent.
Embodiment two：
As shown in Fig. 2, on the basis of embodiment one, present invention implementation provides another semantic analysis, with reality
Applying example one, difference lies in this method further includes：
Step S201：Deep learning model is built based on neural network；
Wherein, neural network can be convolutional neural networks (Convolutional Neural Network, CNN), depth
Spend neural network (Deep Neural Networks, DNN), artificial neural network etc..
Specifically, being trained to artificial neural network by mass data, structure obtains deep learning model.
Step S202：In the training process and/or application process of deep learning model, assisted by manual intervention
Label, to improve the understanding accuracy of participle.
After the step S101 of embodiment one, this method can also include：
Step S203：Record backup is carried out to first voice messaging and carries out reduction of speed processing；
Step S204：Make pauses in reading unpunctuated ancient writings to the first voice messaging；
Specifically, the first voice messaging of end-point detection pair can be based on into punctuate.
Semantic analysis provided in an embodiment of the present invention, handle and make pauses in reading unpunctuated ancient writings by manual intervention, reduction of speed and etc., have
Help improve the accuracy of semantic analysis, while facilitating Record Comparison, deep learning model is corrected, is fed back, improves deep
Spend the applicability of learning model.
Embodiment three：
As shown in figure 3, an embodiment of the present invention provides a kind of semantic analysis devices, including：
Receiving module 301, for receiving the first voice messaging input by user；
Conversion module 302, for converting first voice messaging to text information；
Processing module 303, for being handled the text information using verbal model；
Analysis module 304 carries out semantic point for the deep learning model using structure to treated text information
Analysis generates the semantic understanding result of text information；
Output module 305, for exporting the semantic understanding result of text information.
Further, the conversion module 302 is specifically used for：Judge whether first voice messaging is standard speech message
Breath；If it is not, first voice messaging is then converted to received pronunciation information；Convert the received pronunciation information to word letter
Breath.
Further, the processing module 303 is specifically used for：Using verbal model to text information carry out character segmentation,
Filtering, classification, part of speech analysis, part-of-speech tagging, extraction label, obtain multiple participle phrases.
Further, the analysis module 304 is specifically used for：Utilize the deep learning models coupling application scenarios pair of structure
Treated text information carries out context understanding and semantic disambiguation, generates the semantic understanding result of text information.Specifically,
Context understanding, language are carried out to multiple phrase combination contexts of treated text information using the deep learning model of structure
Justice disambiguates；Obtain the semantic results of multiple phrases；The semantic results of multiple phrases are compared with the phrase of knowledge mapping respectively
It is right, the similarity value of each phrase is obtained, using the highest phrase of similarity value as the semantic results of each phrase, is obtained multiple
The semantic results of phrase；The semantic results of multiple phrases are combined, the semantic understanding result of text information is generated；Alternatively,
Context understanding, language are carried out to multiple phrase combination contexts of treated text information using the deep learning model of structure
Justice disambiguates；Obtain the semantic results of multiple phrases；By the semantic results combination knowledge mapping of multiple phrases, multiple phrases are analyzed
Internal relation and/or logical relation generate the semantic understanding result of text information.
Further, the output module 305 is specifically used for：By the semantic understanding result of text information in the form of word
It is exported；And/or the semantic understanding result of text information is exported in the form of received pronunciation；And/or by word
The semantic understanding result of information is exported in a pattern.
Further, which can also include：Training module 306, for deep learning model training process and/
Or in application process, aid mark is carried out by manual intervention, to improve the understanding accuracy of participle.
Semantic analysis device provided in an embodiment of the present invention has identical with the semantic analysis that above-described embodiment provides
Technical characteristic reach identical technique effect so can also solve identical technical problem.
The technique effect and preceding method embodiment phase of the device that the embodiment of the present invention is provided, realization principle and generation
Together, to briefly describe, device embodiment part does not refer to place, can refer to corresponding contents in preceding method embodiment.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
It should be noted that above-mentioned various models, including dialect model, verbal model, deep learning model
It can be trained and be generated by neural network learning Deng, specifically be referred to the learning process of neural network, details are not described herein.
Referring to Fig. 4, the embodiment of the present invention also provides a kind of electronic equipment 100, including：Processor 40, memory 41, bus
42 and communication interface 43, the processor 40, communication interface 43 and memory 41 connected by bus 42；Processor 40 is for holding
The executable module stored in line storage 41, such as computer program.
Wherein, memory 41 may include high-speed random access memory (RAM, Random Access Memory),
May further include non-volatile memory (non-volatile memory), for example, at least a magnetic disk storage.By extremely
A few communication interface 43 (can be wired or wireless) is realized logical between the system network element and at least one other network element
Letter connection can use internet, wide area network, local network, Metropolitan Area Network (MAN) etc..
Bus 42 can be isa bus, pci bus or eisa bus etc..The bus can be divided into address bus, data
Bus, controlling bus etc..Only indicated with a four-headed arrow for ease of indicating, in Fig. 4, it is not intended that an only bus or
A type of bus.
Wherein, memory 41 is for storing program 401, and the processor 40 is after receiving and executing instruction, described in execution
Program 401, the method performed by device that the stream process that aforementioned any embodiment of the embodiment of the present invention discloses defines can be applied
It is realized in processor 40, or by processor 40.
Processor 40 may be a kind of IC chip, the processing capacity with signal.During realization, above-mentioned side
Each step of method can be completed by the integrated logic circuit of the hardware in processor 40 or the instruction of software form.Above-mentioned
Processor 40 can be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network
Processor (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital Signal
Processing, abbreviation DSP), application-specific integrated circuit (Application Specific Integrated Circuit, referred to as
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, abbreviation FPGA) or other are programmable
Logical device, discrete gate or transistor logic, discrete hardware components.It may be implemented or execute in the embodiment of the present invention
Disclosed each method, step and logic diagram.General processor can be microprocessor or the processor can also be to appoint
What conventional processor etc..The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in hardware decoding processing
Device executes completion, or in decoding processor hardware and software module combination execute completion.Software module can be located at
Machine memory, flash memory, read-only memory, programmable read only memory or electrically erasable programmable memory, register etc. are originally
In the storage medium of field maturation.The storage medium is located at memory 41, and processor 40 reads the information in memory 41, in conjunction with
Its hardware completes the step of above method.
The embodiment of the present invention additionally provides a kind of computer of non-volatile program code that can perform with processor can
Medium is read, said program code makes the processor execute any method of previous embodiment.
Unless specifically stated otherwise, the opposite step of the component and step that otherwise illustrate in these embodiments, digital table
It is not limit the scope of the invention up to formula and numerical value.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined, then it further need not be defined and explained in subsequent attached drawing in a attached drawing.
Flow chart and block diagram in attached drawing show the system, method and computer journey of multiple embodiments according to the present invention
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part for a part for one module, section or code of table, the module, section or code includes one or more uses
The executable instruction of the logic function as defined in realization.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two continuous boxes can essentially base
Originally it is performed in parallel, they can also be executed in the opposite order sometimes, this is depended on the functions involved.It is also noted that
It is the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, can uses and execute rule
The dedicated hardware based system of fixed function or action is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
In addition, in the description of the embodiment of the present invention unless specifically defined or limited otherwise, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
Can also be electrical connection to be mechanical connection；It can be directly connected, can also indirectly connected through an intermediary, Ke Yishi
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
In the description of the present invention, it should be noted that term "center", "upper", "lower", "left", "right", "vertical",
The orientation or positional relationship of the instructions such as "horizontal", "inner", "outside" be based on the orientation or positional relationship shown in the drawings, merely to
Convenient for the description present invention and simplify description, do not indicate or imply the indicated device or element must have a particular orientation,
With specific azimuth configuration and operation, therefore it is not considered as limiting the invention.In addition, term " first ", " second ",
" third " is used for description purposes only, and is not understood to indicate or imply relative importance.
The computer program product for the progress semantic analysis that the embodiment of the present invention is provided, including store processor
The computer readable storage medium of executable non-volatile program code, the instruction that said program code includes can be used for executing
Method described in previous methods embodiment, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.The apparatus embodiments described above are merely exemplary, for example, the division of the unit,
Only a kind of division of logic function, formula that in actual implementation, there may be another division manner, in another example, multiple units or component can
To combine or be desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or beg for
The mutual coupling, direct-coupling or communication connection of opinion can be by some communication interfaces, device or unit it is indirect
Coupling or communication connection can be electrical, machinery or other forms.
The unit illustrated as separating component may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, you can be located at a place, or may be distributed over multiple
In network element.Some or all of unit therein can be selected according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
It is that each unit physically exists alone, it can also be during two or more units be integrated in one unit.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer read/write memory medium of a processor.Based on this understanding, of the invention
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the present invention
State all or part of step of method.And storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with
Store the medium of program code.
Finally it should be noted that：Embodiment described above, only specific implementation mode of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those of ordinary skill in the art that：Any one skilled in the art
In the technical scope disclosed by the present invention, it can still modify to the technical solution recorded in previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of semantic analysis, which is characterized in that including：
Receive the first voice messaging input by user；
Convert first voice messaging to text information；
The text information is handled using verbal model；
The semanteme that semantic analysis generates text information is carried out to treated text information using the deep learning model of structure
Understand result；
The semantic understanding result of text information is exported.
2. according to the method described in claim 1, it is characterized in that, described convert first voice messaging to word letter
Breath, specifically includes：
Judge whether first voice messaging is received pronunciation information；
If it is not, first voice messaging is then converted to received pronunciation information；
Convert the received pronunciation information to text information.
3. according to the method described in claim 1, it is characterized in that, it is described using verbal model to the text information at
Reason, specifically includes：
Character segmentation, filtering, classification, part of speech analysis, part-of-speech tagging, extraction label are carried out to text information using verbal model,
Obtain multiple participle phrases.
4. according to the method described in claim 1, it is characterized in that, the deep learning model using structure is to by handling
Text information carry out semantic analysis generate text information semantic understanding as a result, specifically including：
Context understanding and language are carried out to treated text information using the deep learning models coupling application scenarios of structure
Justice disambiguates, and generates the semantic understanding result of text information.
5. according to the method described in claim 4, it is characterized in that, the deep learning models coupling applied field using structure
Scape carries out context understanding and semantic disambiguation to treated text information, generates the semantic understanding of text information as a result, tool
Body includes：
Unity and coherence in writing up and down is carried out to multiple phrase combination contexts of treated text information using the deep learning model of structure
Solution, semantic disambiguation；Obtain the semantic results of multiple phrases；
The semantic results of multiple phrases are compared with the phrase of knowledge mapping respectively, obtain the similarity value of each phrase,
Using the highest phrase of similarity value as the semantic results of each phrase, the semantic results of multiple phrases are obtained；
The semantic results of multiple phrases are combined, the semantic understanding result of text information is generated.
6. according to the method described in claim 4, it is characterized in that, the deep learning models coupling applied field using structure
Scape carries out context understanding and semantic disambiguation to treated text information, generates the semantic understanding of text information as a result, tool
Body includes：
Unity and coherence in writing up and down is carried out to multiple phrase combination contexts of treated text information using the deep learning model of structure
Solution, semantic disambiguation；Obtain the semantic results of multiple phrases；
By the semantic results combination knowledge mapping of multiple phrases, the internal relation and/or logical relation of multiple phrases are analyzed, is generated
The semantic understanding result of text information.
7. according to the method described in claim 1, it is characterized in that, the semantic understanding result progress by text information is defeated
Go out, specifically includes：
The semantic understanding result of text information is exported in the form of word；
And/or the semantic understanding result of text information is exported in the form of received pronunciation；
And/or the semantic understanding result of text information is exported in the form of picture；
And/or the semantic understanding result of text information is exported in the form of video；
And/or the semantic understanding result of text information is exported in the form of hyperlink.
8. according to the method described in claim 1, it is characterized in that, further including：
In the training process and/or application process of the deep learning model, aid mark is carried out by manual intervention, to carry
The understanding accuracy of height participle.
9. a kind of semantic analysis device, which is characterized in that including：
Receiving module, for receiving the first voice messaging input by user；
Conversion module, for converting first voice messaging to text information；
Processing module, for being handled the text information using verbal model；
Analysis module carries out semantic analysis to treated text information for the deep learning model using structure and generates text
The semantic understanding result of word information；
Output module, for exporting the semantic understanding result of text information.
10. a kind of electronic equipment, including memory, processor and it is stored on the memory and can transports on the processor
Capable computer program, which is characterized in that the processor realizes the claims 1 to 8 when executing the computer program
The step of any one of them method.
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