CN111143527A - Automatic question answering method, device and equipment based on man-machine conversation - Google Patents
Automatic question answering method, device and equipment based on man-machine conversation Download PDFInfo
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Abstract
The invention discloses an automatic question answering method, device and equipment based on man-machine conversation. Wherein the method comprises the following steps: extracting characteristic words from an original dialog in the human-computer dialog by adopting a convolutional neural network mode, classifying the extracted characteristic words, labeling the word classification in the extracted characteristic words by adopting a question-answer matching mode, establishing a matching relation model between question-answer pairs related to the human-computer dialog according to the word classification labeled in the characteristic words, establishing a question-answer pair database based on the human-computer dialog according to the established matching relation model, and automatically replying the human-computer dialog according to the established question-answer pair database based on the human-computer dialog. By the aid of the method, automatic question answering and answering response to man-machine conversation can be achieved without manual work, labor cost is reduced, and question answering and answering response efficiency is improved.
Description
Technical Field
The invention relates to the technical field of man-machine conversation, in particular to an automatic question answering method, device and equipment based on man-machine conversation.
Background
With the development of artificial intelligence, man-machine conversation has been developed in the fields of intelligent home, intelligent assistant and the like. Particularly, in recent years, with the increase of the scale of deep learning technology, natural language processing technology and artificially constructed knowledge base, a great amount of research results and schemes emerge from man-machine conversation.
However, the existing automatic question-answering schemes based on human-computer conversation generally use manual work to reply questions and answers, and have high labor cost and general question-answering reply efficiency.
Disclosure of Invention
In view of this, the present invention provides an automatic question answering method, device and apparatus based on human-computer conversation, which can automatically perform question answering reply to human-computer conversation without manual work, reduce labor cost and improve efficiency of question answering reply.
According to one aspect of the invention, an automatic question answering method based on man-machine conversation is provided, which comprises the following steps: extracting feature words from an original dialog in the man-machine dialog by adopting a convolutional neural network mode; carrying out word classification on the extracted characteristic words; labeling the word classification in the extracted characteristic words in a question-answer matching mode; establishing a matching relation model between question-answer pairs associated with the man-machine conversation according to the word classification marked in the characteristic words; constructing a question-answer database based on man-machine conversation according to the established matching relation model; and automatically replying the question and answer to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
Wherein, adopt the convolutional neural network mode, carry out the extraction of feature word to the original dialogue in the people-computer dialogue, include: the method comprises the steps of applying a convolutional neural network to a text classification task of an original dialog in the man-machine dialog, and extracting feature words of the original dialog in the man-machine dialog in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task.
Wherein, the establishing of a matching relationship model between question-answer pairs associated with a man-machine conversation according to the word classification labeled in the feature words comprises: and inputting the man-machine conversation into the convolutional neural network according to the word classification marked in the characteristic words, dynamically distributing attention weight by adopting an attention mechanism mode, and establishing a matching relation model between question-answer pairs related to the man-machine conversation by adopting a semantic association mode through the convolutional neural network.
Wherein, according to the established matching relation model, a question-answer database based on man-machine conversation is established, comprising: and according to the established matching relation model, typing words and phrases on each question-answer pair of the man-machine conversation, and according to the typed words and phrases, constructing a question-answer pair database based on the man-machine conversation.
After the automatic question-answer reply is performed on the human-computer conversation according to the constructed question-answer database based on the human-computer conversation, the method further comprises the following steps: and optimizing the constructed question-answer pairs based on the man-machine conversation in the database by adopting a best matching question-answer pair mode.
According to another aspect of the present invention, there is also provided an automatic question answering apparatus based on a man-machine conversation, including: the system comprises an extraction module, a classification module, a labeling module, an establishing module, a construction module and a reply module; the extraction module is used for extracting the characteristic words of the original dialog in the man-machine dialog in a convolutional neural network mode; the classification module is used for carrying out word classification on the extracted characteristic words; the labeling module is used for classifying and labeling the words in the extracted characteristic words in a question-answer matching mode; the establishing module is used for establishing a matching relation model between question and answer pairs related to the man-machine conversation according to the word classification marked in the characteristic words; the building module is used for building a question-answer database based on man-machine conversation according to the established matching relation model; and the reply module is used for automatically replying questions and answers to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
Wherein, the extraction module is specifically configured to: the method comprises the steps of applying a convolutional neural network to a text classification task of an original dialog in the man-machine dialog, and extracting feature words of the original dialog in the man-machine dialog in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task.
Wherein the establishing module is specifically configured to: and inputting the man-machine conversation into the convolutional neural network according to the word classification marked in the characteristic words, dynamically distributing attention weight by adopting an attention mechanism mode, and establishing a matching relation model between question-answer pairs related to the man-machine conversation by adopting a semantic association mode through the convolutional neural network.
Wherein the building block is specifically configured to: and according to the established matching relation model, typing words and phrases on each question-answer pair of the man-machine conversation, and according to the typed words and phrases, constructing a question-answer pair database based on the man-machine conversation.
Wherein, the automatic question answering device based on man-machine conversation still includes: an optimization module; and the optimization module is used for optimizing the question-answer pairs of the man-machine conversation in the database by adopting a best matching question-answer pair mode based on the constructed question-answer pairs of the man-machine conversation.
It can be found that, in the above scheme, the convolutional neural network mode can be adopted to extract the characteristic words of the original dialog in the human-computer dialog, and the extracted characteristic words can be classified, and the question-answer matching mode can be adopted to label the word classification in the extracted characteristic words, and the matching relation model between the question-answer pairs related to the human-computer dialog can be established according to the word classification labeled in the characteristic words, and the question-answer database based on the human-computer dialog can be established according to the established matching relation model, and the automatic question-answer reply can be performed on the human-computer dialog according to the established question-answer database based on the human-computer dialog.
Furthermore, according to the scheme, the convolutional neural network can be applied to a text classification task of an original dialog in the man-machine dialog, and the original dialog in the man-machine dialog is subjected to feature word extraction in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task, so that the advantage of better extracting local correlation of original dialog feature words in the man-machine dialog can be realized.
Furthermore, according to the scheme, the human-computer conversation can be input into the convolutional neural network according to the word classification marked in the characteristic words, the attention weight is dynamically distributed in an attention mechanism mode, and a matching relationship model between question-answer pairs related to the human-computer conversation is established in a semantic association mode through the convolutional neural network.
Furthermore, the scheme can be used for typing words and phrases into each question and answer pair of the man-machine conversation according to the established matching relation model, and constructing the question and answer database based on the man-machine conversation according to the typed words and phrases, so that the advantage of accurately constructing the question and answer database based on the man-machine conversation can be realized.
Furthermore, the scheme can adopt a best matching question-answer pair mode to optimize the constructed question-answer pair of the man-machine conversation in the database based on the man-machine conversation, and the method has the advantage of improving the accuracy rate of automatically performing question-answer reply on the man-machine conversation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of an automatic question answering method based on man-machine interaction according to the present invention;
FIG. 2 is a flow chart of an automatic question answering method based on man-machine interaction according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of an automatic question answering device based on human-computer interaction according to the present invention;
FIG. 4 is a schematic structural diagram of an automatic question answering device based on man-machine interaction according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of an embodiment of the automatic question-answering device based on man-machine conversation according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be noted that the following examples are only illustrative of the present invention, and do not limit the scope of the present invention. Similarly, the following examples are only some but not all examples of the present invention, and all other examples obtained by those skilled in the art without any inventive work are within the scope of the present invention.
The invention provides an automatic question-answering method based on human-computer conversation, which can automatically answer the question of the human-computer conversation without manual work, reduce the labor cost and improve the efficiency of answer-answering.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an automatic question answering method based on man-machine conversation according to the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
s101: and performing feature word extraction on an original dialog in the man-machine dialog by adopting a CNN (Convolutional Neural Networks) mode.
Wherein, should adopt the convolutional neural network mode, carry out the extraction of feature word to the original dialogue in the people-computer dialogue, can include:
the method has the advantages that the convolutional neural network is applied to the text classification task of the original dialog in the man-machine dialog, and the key information in the original dialog is extracted by adopting the mode that at least two different Kernel kernels of Batchsize applied to the convolutional neural network in the text classification task are adopted to extract the key information in the original dialog, so that the characteristic words are extracted from the original dialog in the man-machine dialog, and the method has the advantage that the local correlation of the characteristic words of the original dialog in the man-machine dialog can be better extracted.
S102: and carrying out word classification on the extracted characteristic words.
In this embodiment, the word classification may be a guide word, a consultation word, a presentation word, an introduction word, and the like, and the present invention is not limited thereto.
S103: and adopting a question-answer matching mode to label the word classification in the extracted characteristic words.
In this embodiment, a question-answer matching mode of annotating question words, reply words, and word classifications may be adopted to label the word classifications in the extracted feature words, which is advantageous for establishing a matching relationship model between question-answer pairs of a man-machine conversation.
S104: and establishing a matching relation model between question-answer pairs of the associated man-machine conversation according to the word classification marked in the characteristic words.
The establishing of the matching relationship model between question-answer pairs associated with the human-computer conversation according to the word classification labeled in the feature words may include:
according to the word classification marked in the characteristic words, the man-machine conversation is input into the convolutional neural network, the attention weight is dynamically distributed in an attention mechanism mode, and a matching relation model between question-answer pairs related to the man-machine conversation is established in a semantic association mode through the convolutional neural network.
S105: and constructing a question-answer database based on man-machine conversation according to the established matching relation model.
Wherein, should construct the question-answer pair database based on man-machine conversation according to the matching relationship model that should establish, can include:
according to the established matching relation model, words are classified on each question-answer pair of the man-machine conversation, and a question-answer database based on the man-machine conversation is established according to the classified words.
S106: and automatically replying the question and answer to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
After the automatic question-answer reply is performed on the human-computer conversation according to the constructed question-answer database based on the human-computer conversation, the method can further comprise the following steps:
by adopting the mode of best matching question-answer pairs, the constructed question-answer pairs based on the man-machine conversation in the database are optimized, so that the advantage of improving the accuracy rate of automatically replying the question-answer to the man-machine conversation is realized.
It can be found that, in this embodiment, a convolutional neural network mode may be adopted to extract the feature words of the original dialog in the human-computer dialog, and may classify the extracted feature words, and a question-answer matching mode may be adopted to label the word classification in the extracted feature words, and may establish a matching relationship model between question-answer pairs associated with the human-computer dialog according to the word classification labeled in the feature words, and may establish a question-answer database based on the human-computer dialog according to the established matching relationship model, and may automatically perform a question-answer on the human-computer dialog according to the established question-answer database based on the human-computer dialog, which may achieve an automatic question-answer reply on the human-computer dialog without manual work, reduce the labor cost, and improve the efficiency of the question-answer reply.
Further, in this embodiment, the convolutional neural network may be applied to a text classification task of an original dialog in a human-computer dialog, and feature words are extracted from the original dialog in the human-computer dialog in a manner of extracting key information in the original dialog by using at least two different batch-sized kernels in the convolutional neural network applied to the text classification task, which has the advantage of being able to achieve better local correlation for extracting feature words of the original dialog in the human-computer dialog.
Further, in this embodiment, the human-computer conversation may be input into the convolutional neural network according to the word classification labeled in the feature word, the attention weight is dynamically assigned by using an attention mechanism, and a matching relationship model between question-answer pairs associated with the human-computer conversation is established by using the convolutional neural network in a semantic association manner, which has the advantage of improving the accuracy of the matching relationship between question-answer pairs associated with the human-computer conversation.
Further, in this embodiment, a word classification may be made for each question and answer pair of the human-computer conversation according to the established matching relationship model, and a question and answer database based on the human-computer conversation may be constructed according to the made word classification.
Referring to fig. 2, fig. 2 is a schematic flow chart of an automatic question answering method based on man-machine conversation according to another embodiment of the present invention. In this embodiment, the method includes the steps of:
s201: and extracting characteristic words from the original dialog in the man-machine dialog by adopting a convolutional neural network mode.
As described above in S101, further description is omitted here.
S202: and carrying out word classification on the extracted characteristic words.
As described above in S102, further description is omitted here.
S203: and adopting a question-answer matching mode to label the word classification in the extracted characteristic words.
As described above in S103, which is not described herein.
S204: and establishing a matching relation model between question-answer pairs of the associated man-machine conversation according to the word classification marked in the characteristic words.
As described above in S104, and will not be described herein.
S205: and constructing a question-answer database based on man-machine conversation according to the established matching relation model.
As described above in S105, which is not described herein.
S206: and automatically replying the question and answer to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
As described above in S106, and will not be described herein.
S207: and optimizing the constructed question-answer pairs based on the man-machine conversation in the database by adopting a best matching question-answer pair mode.
It can be found that in this embodiment, a best matching question-answer pair mode may be adopted, and the constructed question-answer pair based on the human-computer conversation in the database is optimized, so that the advantage of being able to improve the accuracy of automatically performing question-answer reply on the human-computer conversation is provided.
The invention also provides an automatic question answering device based on the man-machine conversation, which can automatically answer the question of the man-machine conversation without manual work, reduces the labor cost and improves the efficiency of answer.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of an automatic question answering device based on man-machine interaction according to the present invention. In this embodiment, the automatic question-answering device 30 based on human-computer conversation includes an extracting module 31, a classifying module 32, a labeling module 33, an establishing module 34, a constructing module 35 and a replying module 36.
The extraction module 31 is configured to extract feature words from an original dialog in the human-computer dialog by using a convolutional neural network.
The classification module 32 is configured to perform word classification on the extracted feature words.
The labeling module 33 is configured to label the word classification in the extracted feature word in a question-answer matching manner.
The establishing module 34 is configured to establish a matching relationship model between question-answer pairs associated with the human-computer conversation according to the word classification labeled in the feature words.
The building module 35 is configured to build a question-answer database based on a human-computer conversation according to the established matching relationship model.
The reply module 36 is configured to perform automatic question-answer reply on the human-computer conversation according to the constructed question-answer database based on the human-computer conversation.
Optionally, the extracting module 31 may be specifically configured to:
the convolutional neural network is applied to a text classification task of an original dialog in the man-machine dialog, and the original dialog in the man-machine dialog is subjected to feature word extraction in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task.
Optionally, the establishing module 34 may be specifically configured to:
and inputting the man-machine conversation into the convolutional neural network according to the word classification labeled in the characteristic words, dynamically distributing attention weight by adopting an attention mechanism mode, and establishing a matching relation model between question-answer pairs related to the man-machine conversation by adopting a semantic association mode through the convolutional neural network.
Optionally, the building module 35 may be specifically configured to:
and according to the established matching relation model, typing words and phrases on each question and answer pair of the man-machine conversation, and according to the typing words and phrases, constructing a question and answer database based on the man-machine conversation.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an automatic question answering device based on man-machine interaction according to another embodiment of the present invention. Unlike the previous embodiment, the automatic question-answering device 40 based on human-computer interaction according to the present embodiment further includes an optimization module 41.
The optimizing module 41 is configured to optimize the constructed question-answer pairs based on the human-computer conversation in the database by using a best matching question-answer pair manner.
Each unit module of the automatic question answering device 30/40 based on human-computer conversation can respectively execute the corresponding steps in the above method embodiments, so the description of each unit module is omitted here, and please refer to the description of the corresponding steps above in detail.
The present invention further provides an automatic question answering apparatus based on a man-machine conversation, as shown in fig. 5, including: at least one processor 51; and a memory 52 communicatively coupled to the at least one processor 51; the memory 52 stores instructions executable by the at least one processor 51, and the instructions are executed by the at least one processor 51, so that the at least one processor 51 can execute the above-mentioned automatic question answering method based on man-machine interaction.
Wherein the memory 52 and the processor 51 are coupled in a bus, which may comprise any number of interconnected buses and bridges, which couple one or more of the various circuits of the processor 51 and the memory 52 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 51 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 51.
The processor 51 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory 52 may be used to store data used by the processor 51 in performing operations.
The present invention further provides a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
It can be found that, in the above scheme, the convolutional neural network mode can be adopted to extract the characteristic words of the original dialog in the human-computer dialog, and the extracted characteristic words can be classified, and the question-answer matching mode can be adopted to label the word classification in the extracted characteristic words, and the matching relation model between the question-answer pairs related to the human-computer dialog can be established according to the word classification labeled in the characteristic words, and the question-answer database based on the human-computer dialog can be established according to the established matching relation model, and the automatic question-answer reply can be performed on the human-computer dialog according to the established question-answer database based on the human-computer dialog.
Furthermore, according to the scheme, the convolutional neural network can be applied to a text classification task of an original dialog in the man-machine dialog, and the original dialog in the man-machine dialog is subjected to feature word extraction in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task, so that the advantage of better extracting local correlation of original dialog feature words in the man-machine dialog can be realized.
Furthermore, according to the scheme, the human-computer conversation can be input into the convolutional neural network according to the word classification marked in the characteristic words, the attention weight is dynamically distributed in an attention mechanism mode, and a matching relationship model between question-answer pairs related to the human-computer conversation is established in a semantic association mode through the convolutional neural network.
Furthermore, the scheme can be used for typing words and phrases into each question and answer pair of the man-machine conversation according to the established matching relation model, and constructing the question and answer database based on the man-machine conversation according to the typed words and phrases, so that the advantage of accurately constructing the question and answer database based on the man-machine conversation can be realized.
Furthermore, the scheme can adopt a best matching question-answer pair mode to optimize the constructed question-answer pair of the man-machine conversation in the database based on the man-machine conversation, and the method has the advantage of improving the accuracy rate of automatically performing question-answer reply on the man-machine conversation.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a part of the embodiments of the present invention, and not intended to limit the scope of the present invention, and all equivalent devices or equivalent processes performed by the present invention through the contents of the specification and the drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An automatic question-answering method based on man-machine conversation is characterized by comprising the following steps:
extracting feature words from an original dialog in the man-machine dialog by adopting a convolutional neural network mode;
carrying out word classification on the extracted characteristic words;
labeling the word classification in the extracted characteristic words in a question-answer matching mode;
establishing a matching relation model between question-answer pairs associated with the man-machine conversation according to the word classification marked in the characteristic words;
constructing a question-answer database based on man-machine conversation according to the established matching relation model;
and automatically replying the question and answer to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
2. The automatic question-answering method based on man-machine conversation according to claim 1, wherein the extracting of the feature words of the original conversation in the man-machine conversation by using the convolutional neural network mode comprises:
the method comprises the steps of applying a convolutional neural network to a text classification task of an original dialog in the man-machine dialog, and extracting feature words of the original dialog in the man-machine dialog in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task.
3. The method according to claim 1, wherein the establishing a matching relationship model between question-answer pairs associated with human-computer conversation according to the word classification labeled in the feature words comprises:
and inputting the man-machine conversation into the convolutional neural network according to the word classification marked in the characteristic words, dynamically distributing attention weight by adopting an attention mechanism mode, and establishing a matching relation model between question-answer pairs related to the man-machine conversation by adopting a semantic association mode through the convolutional neural network.
4. The method according to claim 1, wherein the constructing a question-answer database based on human-machine conversation according to the established matching relationship model comprises:
and according to the established matching relation model, typing words and phrases on each question-answer pair of the man-machine conversation, and according to the typed words and phrases, constructing a question-answer pair database based on the man-machine conversation.
5. The method according to claim 1, further comprising, after the automatic question-answer reply to the human-computer conversation from the constructed question-answer database based on human-computer conversation, the steps of:
and optimizing the constructed question-answer pairs based on the man-machine conversation in the database by adopting a best matching question-answer pair mode.
6. An automatic question answering device based on man-machine conversation is characterized by comprising:
the system comprises an extraction module, a classification module, a labeling module, an establishing module, a construction module and a reply module;
the extraction module is used for extracting the characteristic words of the original dialog in the man-machine dialog in a convolutional neural network mode;
the classification module is used for carrying out word classification on the extracted characteristic words;
the labeling module is used for classifying and labeling the words in the extracted characteristic words in a question-answer matching mode;
the establishing module is used for establishing a matching relation model between question and answer pairs related to the man-machine conversation according to the word classification marked in the characteristic words;
the building module is used for building a question-answer database based on man-machine conversation according to the established matching relation model;
and the reply module is used for automatically replying questions and answers to the human-computer conversation according to the constructed question and answer database based on the human-computer conversation.
7. The automatic question-answering device based on man-machine conversation according to claim 6, characterized in that said extraction module is specifically configured to:
the method comprises the steps of applying a convolutional neural network to a text classification task of an original dialog in the man-machine dialog, and extracting feature words of the original dialog in the man-machine dialog in a mode of extracting key information in the original dialog by adopting at least two different batch-size kernels in the convolutional neural network applied to the text classification task.
8. The automatic question-answering device based on man-machine conversation according to claim 6, characterized in that said establishment module is specifically configured to:
and inputting the man-machine conversation into the convolutional neural network according to the word classification marked in the characteristic words, dynamically distributing attention weight by adopting an attention mechanism mode, and establishing a matching relation model between question-answer pairs related to the man-machine conversation by adopting a semantic association mode through the convolutional neural network.
9. The automatic question-answering device based on human-computer conversation according to claim 6, characterized in that said building module is specifically configured to:
and according to the established matching relation model, typing words and phrases on each question-answer pair of the man-machine conversation, and according to the typed words and phrases, constructing a question-answer pair database based on the man-machine conversation.
10. The human-machine conversation-based automatic question-answering device according to claim 6, wherein said human-machine conversation-based automatic question-answering device further comprises:
an optimization module;
and the optimization module is used for optimizing the question-answer pairs of the man-machine conversation in the database by adopting a best matching question-answer pair mode based on the constructed question-answer pairs of the man-machine conversation.
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