CN111400340A - Natural language processing method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention is suitable for the technical field of artificial intelligence and provides a natural language processing method, a device, computer equipment and a storage medium, wherein the natural language processing method comprises the following steps: acquiring natural language information input by a user; identifying the natural language information and determining user intention information; extracting keywords from the user intention information; and determining a label matched with the keyword in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to a user. On the one hand, the interference of the traditional word segmentation technology to language processing is reduced, the accuracy rate of natural language understanding is greatly improved, on the other hand, the unique embedding technology is adopted, and the method is popular, is more friendly and humanized on the human-computer interaction result, and is favorable for better service customers.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a natural language processing method, a natural language processing device, computer equipment and a storage medium.
Background
The man-machine interaction refers to the process of information exchange between a person and a computer for completing a determined task in a certain interaction mode by using a certain dialogue language between the person and the computer.
In recent years, human-computer interaction has advanced in various fields, and is gradually replacing graphical user interfaces to become new user interfaces. However, due to the limitation of the adopted technology, when the current human-computer interaction aims at the processing of natural language, more single models are adopted for processing, the related industries are single, the results are general for data expression of different industries, and for Chinese language, a word segmentation technology is often needed, the expressive force of the models is greatly influenced, simple conversation and simple processing can be performed only, the intention of a user cannot be effectively understood under certain conditions, when an unanswerable problem or an unintelligible expression is encountered, a search engine is generally simply called to search keywords in the input of the user, and a webpage of the search result is directly returned to the user, so that the better service of the user is not facilitated.
Therefore, the existing natural language processing method is single in related industry, general in data expression result of different industries, and often needs to use word segmentation technology, so that the expressive force of the model is greatly influenced, and the method is not beneficial to better serving customers.
Disclosure of Invention
The embodiment of the invention aims to provide a natural language processing method, and aims to solve the problems that the existing natural language processing method is single in related industry, general in data expression results of different industries, and often needs to rely on word segmentation technology, has great influence on the expression of a model, and is not beneficial to better serving customers.
The embodiment of the invention is realized in such a way that a natural language processing method comprises the following steps:
acquiring natural language information input by a user;
identifying the natural language information and determining user intention information;
extracting keywords from the user intention information;
and determining a label matched with the keyword in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to a user.
Another object of an embodiment of the present invention is to provide a natural language processing apparatus, including:
an acquisition unit configured to acquire natural language information of a user;
an intention determining unit for recognizing the natural language information and determining user intention information;
a keyword extraction unit, configured to perform keyword extraction on the user intention information; and
and the output unit is used for determining the label matched with the keyword in the knowledge base, acquiring the matching answer corresponding to the determined label and outputting the matching answer to the user.
It is another object of an embodiment of the present invention to provide a computer apparatus, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the natural language processing method.
Another object of an embodiment of the present invention is a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, causes the processor to execute the steps of the natural language processing method.
The natural language processing method provided by the embodiment of the invention identifies the natural language information input by the user and determines the intention information of the user; further determining a label matched with the keyword of the user intention information in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to the user; on the one hand, the interference of the traditional word segmentation technology to language processing is reduced, the accuracy rate of natural language understanding is greatly improved, on the other hand, the unique embedding technology is adopted, and the method is popular, is more friendly and humanized on the human-computer interaction result, and is favorable for better service customers.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a natural language processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an implementation of a natural language processing method according to a second embodiment of the present invention;
fig. 3 is a flowchart of an implementation of a natural language processing method according to a third embodiment of the present invention;
fig. 4 is a flowchart of an implementation of a natural language processing method according to a fourth embodiment of the present invention;
fig. 5 is a flowchart of an implementation of a natural language processing method according to a fifth embodiment of the present invention;
fig. 6 is a flowchart of an implementation of a natural language processing method according to a sixth embodiment of the present invention;
fig. 7 is a flowchart of an implementation of a natural language processing method according to a seventh embodiment of the present invention;
fig. 8 is a block diagram of a natural language processing apparatus according to a ninth embodiment of the present invention;
FIG. 9 is a block diagram showing an internal configuration of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, etc. may be used to describe various information in embodiments of the present invention, the information should not be limited by these terms. These terms are only used to distinguish one type of information from another.
The natural language processing method provided by the embodiment of the invention identifies the natural language information input by the user and determines the intention information of the user; further determining a label matched with the keyword of the user intention information in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to the user; on the one hand, the interference of the traditional word segmentation technology to language processing is reduced, the accuracy rate of natural language understanding is greatly improved, on the other hand, the unique embedding technology is adopted, and the method is popular, is more friendly and humanized on the human-computer interaction result, and is favorable for better service customers.
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Fig. 1 shows an implementation flow of a natural language processing method provided in an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:
in step S101, natural language information input by the user is acquired.
In the embodiment of the present invention, the natural language information input by the user may be natural language information in a text format directly input by the user, for example, when the user searches for location information of a related store using a guide board of a mall, the text information input by the user may be analyzed; the natural language information in the voice format input by the user may be recognized by the voice recognition model and processed into the natural language information in the text format, for example, when the user makes a consultation using a telephone, the voice information of the user may be analyzed.
In step S102, the natural language information is identified and user intention information is determined.
In the embodiment of the present invention, the user intention information may be obtained through a preset intention recognition model, and since there are many types of existing intention recognition models, the present invention does not require a specific intention recognition model, and a person skilled in the art may select a suitable intention recognition model according to actual requirements, for example, an intention recognition model based on a path feature constructed by a semantic analysis tree, or an intention recognition model based on a convolutional neural network of an attention mechanism.
In the embodiment of the invention, the natural language information is identified and the user intention information is determined, and the information belongs to the professional industry field after the natural language information is subjected to text identification, and the user intention information is identified by combining with the determined user intention identification model in the professional industry field.
In step S103, keyword extraction is performed on the user intention information.
In the embodiment of the present invention, it is,
in step S104, a tag matching the keyword is determined in the knowledge base, and a matching answer corresponding to the determined tag is obtained and output to the user.
In the embodiment of the present invention, it is,
the natural language processing method provided by the embodiment of the invention identifies the natural language information input by the user and determines the intention information of the user; further determining a label matched with the keyword of the user intention information in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to the user; on the one hand, the interference of the traditional word segmentation technology to language processing is reduced, the accuracy rate of natural language understanding is greatly improved, on the other hand, the unique embedding technology is adopted, and the method is popular, is more friendly and humanized on the human-computer interaction result, and is favorable for better service customers.
Fig. 2 shows an implementation flow of a natural language processing method provided in the second embodiment of the present invention, and for convenience of description, only the parts related to the second embodiment of the present invention are shown, which are similar to the first embodiment, except that the step 102 specifically includes:
in step S201, the natural language information is identified and a semantic representation is generated.
In the embodiment of the present invention, one implementation manner of identifying the natural language information and generating the semantic representation may be to preprocess the natural language information and convert it into a structured query language (SQ L statement) for identifying the industry and the requirement information thereof.
In step S202, a text classification is determined from the semantic representation.
In an embodiment of the present invention, one implementation manner of determining the text classification according to the semantic representation may be to determine, according to the converted structured query language, a professional industry field to which the natural language information belongs.
In step S203, user intention information is determined from the semantic representation and the text classification.
According to the natural language processing method provided by the embodiment of the invention, the natural language information is firstly identified and semantic representation is generated, and then professional field identification processing is carried out on the semantic representation, after the professional field to which the natural language information belongs is obtained, the natural language information can be timely positioned into the corresponding intention identification model corresponding to the professional field for processing, and the accuracy of user intention identification is effectively improved.
Fig. 3 shows an implementation flow of a natural language processing method provided by the third embodiment of the present invention, and for convenience of description, only the parts related to the second embodiment of the present invention are shown, which is similar to the second embodiment, except that the step S203 includes the following steps:
in step S301, the semantic representation is converted into a semantic vector based on the text classification.
In the embodiment of the present invention, based on the text classification, the semantic representations may be converted into semantic vectors by locating in time in corresponding professional technical fields, converting the semantic representations in each time sequence into word vectors in corresponding time states, modeling the obtained word vectors by a long-and-short memory network (L STM), and using the last time state vector as a representation of the whole sentence, i.e., a sentence vector.
In the embodiment of the present invention, the text input of the user is converted into vector input, the word2vec (word vector) training is usually performed by using the user chat log, and then the user input is converted by an Embedding layer, that is, each word input by the user (input after word segmentation) is converted into a vector by an Embedding layer, which is a processing method common in the industry and is not described herein again.
In step S302, probability distribution information of the user intention is determined according to the semantic vector and a preset training model, and is output.
In the embodiment of the present invention, the preset training model may be a neural network, which is a concept, and a generic term of a class of algorithms may use rnn (cyclic neural network), cnn (convolutional neural network) and a fully-connected network.
In step S303, the user intention with the largest probability distribution threshold is determined as the user intention information.
Fig. 4 shows an implementation flow of a natural language processing method provided by the fourth embodiment of the present invention, and for convenience of description, only a part related to the fourth embodiment of the present invention is shown, which is similar to the first embodiment, except that the step S104 specifically includes the following steps:
in step S401, it is determined whether the matching degree between the tag in the knowledge base and the keyword exceeds a preset matching threshold; if yes, go to step S402; if not, the process proceeds to step S403.
In step S402, a matching answer corresponding to the label that exactly matches the keyword is obtained and output to the user.
In step S403, a matching answer corresponding to at least one label in fuzzy matching with the keyword is obtained, the matching answer is calculated, and the processed matching answer is output to the user.
Fig. 5 shows an implementation flow of a natural language processing method provided by the fifth embodiment of the present invention, and for convenience of description, only a part related to the fifth embodiment of the present invention is shown, which is similar to the first embodiment, except that the step S104 specifically includes the following steps:
in step S501, the keyword is analyzed to obtain a related word having the same word sense as the keyword.
In step S502, a tag matching the relevant word is determined in the knowledge base, and a matching answer corresponding to the determined tag is obtained and output to the user.
Fig. 6 shows an implementation flow of a natural language processing method provided by a sixth embodiment of the present invention, and for convenience of description, only a part related to the embodiment of the present invention is shown, which is similar to the embodiment, except that the method further includes the following steps:
in step S601, when it is determined that the user is not satisfied with the matching answer, a manual addition reminder is output, and the received correct matching answer is used as a training set.
In step S602, when it is determined that the user is satisfied with the matching answer, the matching answer is used as a verification set.
In step S603, according to the training set and the verification set, performing optimization processing on the preset training model according to a preset optimization cycle.
Fig. 7 shows an implementation flow of a natural language processing method provided by the seventh embodiment of the present invention, and for convenience of description, only a part related to the seventh embodiment of the present invention is shown, which is similar to the seventh embodiment, except that the method further includes the following steps:
in step S701, the knowledge base and the labels thereof are updated according to the training set and the verification set.
In the embodiment of the invention, the wrong matching answers are adjusted to be used as a training set, the correct answers are continued to be used as a verification set, and the knowledge base and the labels thereof are updated, so that the knowledge base can be reversely optimized, and the accuracy and the stability of the matching result are further improved.
Fig. 8 shows a structure of a natural language processing device 800 according to an eighth embodiment of the present invention, and for convenience of explanation, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
the natural language processing apparatus 800 includes an acquisition unit 801, an intention determination unit 802, a keyword extraction unit 803, and an output unit 804.
An obtaining unit 801 is used for obtaining natural language information of a user.
In the embodiment of the present invention, the natural language information input by the user may be natural language information in a text format directly input by the user, for example, when the user searches for location information of a related store using a guide board of a mall, the text information input by the user may be analyzed; the natural language information in the voice format input by the user may be recognized by the voice recognition model and processed into the natural language information in the text format, for example, when the user makes a consultation using a telephone, the voice information of the user may be analyzed.
An intention determining unit 802 for identifying the natural language information and determining user intention information.
In the embodiment of the present invention, the user intention information may be obtained through a preset intention recognition model, and since there are many types of existing intention recognition models, the present invention does not require a specific intention recognition model, and a person skilled in the art may select a suitable intention recognition model according to actual requirements, for example, an intention recognition model based on a path feature constructed by a semantic analysis tree, or an intention recognition model based on a convolutional neural network of an attention mechanism.
In the embodiment of the invention, the natural language information is identified and the user intention information is determined, and the information belongs to the professional industry field after the natural language information is subjected to text identification, and the user intention information is identified by combining with the determined user intention identification model in the professional industry field.
A keyword extraction unit 803, configured to perform keyword extraction on the user intention information.
And the output unit 804 is configured to determine a tag matched with the keyword in a knowledge base, acquire a matching answer corresponding to the determined tag, and output the matching answer to the user.
The natural language processing device provided by the embodiment of the invention identifies the natural language information input by the user and determines the user intention information; further determining a label matched with the keyword of the user intention information in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to the user; on the one hand, the interference of the traditional word segmentation technology to language processing is reduced, the accuracy rate of natural language understanding is greatly improved, on the other hand, the unique embedding technology is adopted, and the method is popular, is more friendly and humanized on the human-computer interaction result, and is favorable for better service customers.
FIG. 9 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a terminal (or server). As shown in fig. 9, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement the natural language processing method. The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform the natural language processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the natural language processing apparatus provided in the present application may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 9. The memory of the computer device may store therein various program modules constituting the natural language processing apparatus, such as the acquisition unit 801, the intention determination unit 802, the keyword extraction unit 803, and the output unit 804 shown in fig. 8. The computer program constituted by the respective program modules causes the processor to execute the steps in the natural language processing method of the respective embodiments of the present application described in the present specification.
For example, the computer device shown in fig. 9 may execute step S101 by the acquisition unit 801 in the natural language processing apparatus shown in fig. 8. The computer device may perform step S102 through the intention determining unit 802. The computer apparatus may perform step S103 through the keyword extraction unit 803. The computer apparatus may perform step S104 through the output unit 804.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring natural language information input by a user;
identifying the natural language information and determining user intention information;
extracting keywords from the user intention information;
and determining a label matched with the keyword in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to a user.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of:
acquiring natural language information input by a user;
identifying the natural language information and determining user intention information;
extracting keywords from the user intention information;
and determining a label matched with the keyword in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to a user.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
Those skilled in the art will appreciate that all or a portion of the processes in the methods of the embodiments described above may be implemented by computer programs that may be stored in a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, non-volatile memory may include read-only memory (ROM), programmable ROM (prom), electrically programmable ROM (eprom), electrically erasable programmable ROM (eeprom), or flash memory, volatile memory may include Random Access Memory (RAM) or external cache memory, RAM is available in a variety of forms, such as static RAM (sram), Dynamic RAM (DRAM), synchronous sdram (sdram), double data rate sdram (ddr sdram), enhanced sdram (sdram), synchronous link (sdram), dynamic RAM (rdram) (rdram L), direct dynamic RAM (rdram), and the like, and/or external cache memory.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A natural language processing method, comprising:
acquiring natural language information input by a user;
identifying the natural language information and determining user intention information;
extracting keywords from the user intention information;
and determining a label matched with the keyword in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to a user.
2. The natural language processing method according to claim 1, wherein the step of identifying the natural language information and determining user intention information specifically comprises:
identifying the natural language information and generating a semantic representation;
determining a text classification from the semantic representation;
and determining user intention information according to the semantic representation and the text classification.
3. The natural language processing method according to claim 2, wherein the step of determining the user intention information based on the semantic representation and the text classification specifically comprises:
converting the semantic representation to a semantic vector based on the text classification;
determining probability distribution information of the user intention according to the semantic vector and a preset training model, and outputting the probability distribution information;
and determining the user intention with the largest probability distribution threshold value as the user intention information.
4. The natural language processing method according to claim 1, wherein the step of determining a tag matching the keyword in a knowledge base, obtaining a matching answer corresponding to the determined tag, and outputting the matching answer to a user specifically includes:
judging whether the matching degree of the labels in the knowledge base and the keywords exceeds a preset matching threshold value or not;
when the matching degree of the labels in the knowledge base and the keywords exceeds a preset matching threshold, acquiring matching answers corresponding to the labels which are accurately matched with the keywords, and outputting the matching answers to a user;
and when the matching degree of the labels in the knowledge base and the keywords does not exceed a preset matching threshold, acquiring matching answers corresponding to at least one label in fuzzy matching with the keywords, calculating the matching answers, and outputting the processed matching answers to the user.
5. The natural language processing method according to claim 1, wherein the step of determining a tag matching the keyword in a knowledge base, obtaining a matching answer corresponding to the determined tag, and outputting the matching answer to a user specifically includes:
analyzing and processing the keywords to obtain associated words with the same word senses as the keywords;
and determining a label matched with the associated word in a knowledge base, acquiring a matching answer corresponding to the determined label, and outputting the matching answer to the user.
6. The natural language processing method according to claim 1, further comprising:
when the matching answer is judged to be unsatisfactory by the user, outputting a manual addition prompt, and taking the received correct matching answer as a training set;
when the user is judged to be satisfied with the matching answer, the matching answer is used as a verification set;
and optimizing the preset training model according to a preset optimization cycle according to the training set and the verification set.
7. The natural language processing method according to claim 6, further comprising:
and updating the knowledge base and the labels thereof according to the training set and the verification set.
8. A natural language processing apparatus, comprising:
an acquisition unit configured to acquire natural language information of a user;
an intention determining unit for recognizing the natural language information and determining user intention information;
a keyword extraction unit, configured to perform keyword extraction on the user intention information; and
and the output unit is used for determining the label matched with the keyword in the knowledge base, acquiring the matching answer corresponding to the determined label and outputting the matching answer to the user.
9. A computer arrangement comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to carry out the steps of the natural language processing method of any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the natural language processing method of any one of claims 1 to 7.
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