KR101851791B1 - Apparatus and method for computing domain diversity using domain-specific terms and high frequency general terms - Google Patents

Abstract

The present invention relates to a domain diversity calculating device and method using a domain-specific term and a high-frequency general term. For this purpose, the domain diversity calculating device comprises: a dividing module receiving query data in the form of a general string, dividing the query data into phrases such as an entity or intent, and processing the query data into vector values; a domain-specific score calculating module comparing the phrase frequency or phrase probability of the query data with the phrase frequency or phrase probability of a general corpus in order to determine whether or not the phrases divided by the dividing module are specific to a specific domain, and then, through the difference, determining a domain-specific score, which is a score regarding how specific a corresponding phrase is to a corresponding domain; a domain-specific term classifying module classifying a domain-specific term, which is a term specific to a corresponding domain, of the phrases of the query data, based on the domain-specific score calculated by the domain-specific score calculating module; and a clustering module clustering the query data into at least one similar sentence group, based on the similarity to the domain-specific term classified by the domain-specific term classifying module. The present invention is able to automatically generate a label so that a manager easily matches queries and answers.

Description

Field of the Invention The present invention relates to a method and apparatus for calculating domain diversity using domain specific terms and high frequency generic terms,

The present invention relates to an apparatus and method for calculating domain diversity using domain-specific terms and high frequency generic terms.

Currently, most companies are not able to provide satisfactory services to their customers due to lack of personnel and infrastructure of the customer support service department. Recently, there have been a lot of companies trying to solve the problem by improving the quality of the customer support service by introducing the chatbot. For example, AT & T calls the CS to let the bot respond.

1 is a schematic diagram showing a basic module configuration of a chatbot. As shown in FIG. 1, the chatbot receives a lexical input composed of a text or voice of a chat in a form that can be processed on a computer by a NLP (Natural Language Processor) module, In response to the Context Analyzer, which analyzes the Context of the natural language, the Decision Maker decides the answer contents according to the Context, classifies the answers and confirms them, and responds to the user according to the determined answers. output.

FIG. 2 is a schematic diagram specifically showing a general chatbot module configuration. As shown in FIG. 2, a general chatbot analyzes a morpheme of a linguistic input inputted with a Morphemic Analyzer attached to a NLP, attaches a Taxonomy Analyzer to a semantically meaningful word, and processes the sentence on a computer It is processed in a form that can be done. The Context Analyzer will match the context in the Context Database through the search engine. The Decision Maker will find the answer to the matching context. Usually, the answer to a specific context is determined by the customer support protocol data template of the particular company that is trying to introduce the chatbot.

FIG. 3 is a schematic diagram specifically showing the configuration of a chatbot module using machine learning. As shown in FIG. 3, a chatbot using machine learning has replaced a lot of elements of a conventional chatbot. Deep-learning model is applied to NLP in the chatbot module configuration using machine learning. Deep learning models applied to NLP can be understood as an early version of NLU (Natural Language Understanding), which is available from Google's SyntexNet published in April 2016. Through SyntexNet, certain verbal input changes to Vector format. We then integrate the Context Analyzer, Decision Maker, and Response Generator to apply the Deep Learning model and continue learning the training set. At least one model such as RNN (Recurrent Neural Network) is used as the deep learning model used in the integration module of Context Analyzer, Decision Maker and Response Generator.

However, these chatbots are not just services that can be implemented by simply planting a few lines of code or providing an API. The introduction of chatbots in a particular company is not just a one-off process of service integration. It is necessary to process the customer support data accumulated in the past as data necessary for the learning of the artificial intelligence module of the chatbot.

Korean Patent Publication No. 10-2017-0021902,

Chatbot is a situation where there is a lot of demand for customer support services, that is, a solution that will deal with the answers to the questions that people do manually. The company that operates the existing customer support is different from the one based on the individual characteristics of the customer support counseling rather than processing the whole process in a single process or a formal format with templates, The same problem is often solved differently.

For this reason, general chatbots should be classified as the same label with the same meaning. Since the response is different, the artificial intelligence engine is like unlabeled data, . It is almost impossible for a person to handle the accumulated data for many years. On the other hand, there is a contradiction in the methodology of reducing the human resources needed for service introduction, that is, introducing chatbots, in order to employ human resources to hire additional human resources or to operate existing services in order to secure human resources to be introduced into the introduction of chattbots.

In addition, even when a company intends to put labor into data processing for chatbots, it is a problem when the context of a query to be judged and processed by humans includes multiple meanings. Because each individual is categorized according to the criteria they judge, they make errors or biases, which also make a serious error that significantly reduces the accuracy of artificial intelligence.

As a result, when the number of data increases, most of the data is unprocessed or processed, but it is incomplete, and training data to be included in artificial intelligence often contains errors and defects.

Previously, we tried to solve this problem by manually creating a dictionary structure of the verb structure and the tagged information of each word, which can be obtained from the data, and labeling them with classified results using a dictionary. However, this method has a problem in that the workload is considerable and additional work is performed on the newly added data.

Therefore, it is an object of the present invention to provide a domain-specific term and a domain using a high-frequency general term, which enables a label to be automatically generated so that an administrator can easily match a question and an answer even when a separately- And an apparatus and method for calculating diversity.

Hereinafter, specific means for achieving the object of the present invention will be described.

An object of the present invention is to provide a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot that receives question data and outputs specific answer data, A classifying module for classifying the question data into entities or intents and processing the question data as vector values; In order to judge whether or not the segmented phrases classified in the classification module are characteristic to a specific domain, the probability of occurrence of a word or occurrence of a word in the question data is compared with the probability of occurrence of a word or occurrence of a word in a general corpus, A domain-specific score calculation module for determining a domain-specific score that is a score of how characteristic a phrase is in the domain; A domain-specific term classification module for classifying a domain-specific term, which is a term specific to the domain, among the phrases of the question data, based on the domain-specific score calculated by the domain-specific score calculation module; And a clustering module for clustering the query data into at least one similar sentence group based on the degree of similarity with the domain-specific terms classified by the domain-specific term classification module, wherein the chatbot manager labels the similar sentence group And providing the at least one similar sentence group generated by the clustering module to the client of the chatbot manager so that the chatbot manager can provide a similar label to the client of the chatbot manager.

Another object of the present invention is to provide a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot receiving question data and outputting specific answer data, A classification module for classifying the question data into entities or intents and processing the question data as vector values; In order to judge whether or not the segmented phrases classified in the classification module are characteristic to a specific domain, the probability of occurrence of a word or occurrence of a word in the question data is compared with the probability of occurrence of a word or occurrence of a word in a general corpus, A domain-specific score calculation module for determining a domain-specific score that is a score of how characteristic a phrase is in the domain; And classifying domain specific terms and general terms specific to the domain among the phrases of the question data based on the domain specific score calculated by the domain specific score calculation module, A high frequency general term classification module for classifying frequency generic terms; And a clustering module for clustering the query data into at least one similar sentence group based on the degree of similarity with the high frequency general term classified by the high frequency general term classification module, And providing the similar sentence group generated by the clustering module to the client of the chatbot manager so that the chatbot can label the chatbot.

An object of the present invention is to provide a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot that receives question data and outputs specific answer data, A classifying module for classifying the question data into entities or intents and processing the question data as vector values; In order to judge whether or not the segmented phrases classified in the classification module are characteristic to a specific domain, the probability of occurrence of a word or occurrence of a word in the question data is compared with the probability of occurrence of a word or occurrence of a word in a general corpus, A domain-specific score calculation module for determining a domain-specific score that is a score of how characteristic a phrase is in the domain; And classifying domain specific terms and general terms specific to the domain among the phrases of the question data based on the domain specific score calculated by the domain specific score calculation module, A high frequency general term classification module for classifying frequency generic terms; A clustering module for clustering the query data into at least one similar sentence group based on the degree of similarity with the high frequency general term classified by the high frequency general term classification module; The clustering module classifies a plurality of sentences included in the similar sentence group clustered on the basis of the degree of similarity with the high frequency general term as second domain classification on the basis of the degree of similarity with the domain specific term to form at least one domain similar sentence group A secondary classification module to classify; A domain diversity calculation module for calculating a domain diversity of the similar sentence group based on the number of the domain similar sentence groups classified in the similar sentence group by the secondary classification module; And a label sentence group determination module that determines the importance of the similar sentence group based on the domain diversity of the similar sentence group calculated by the domain diversity calculation module and determines a label sentence group that is a sentence group to be provided to the chatbot manager And providing the label sentence group to the client of the chatbot manager so that the chatbot administrator can label the label sentence group. The apparatus for generating a virtual label for a training set labeling of a chatbot, .

Another object of the present invention is to provide a virtual label generation method using a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot receiving question data and outputting specific answer data, A classifying module which is a constituent unit of the apparatus receives the question data in a general string form and classifies the question data into entities or intents and processes the question data into vector values; The domain-specific score calculation module, which is a component of the virtual label generation device, calculates a word occurrence frequency or a word occurrence probability in the question data and a word occurrence probability in the general corpus in order to judge whether the word classified in the classification module is characteristic to a specific domain A domain-specific score calculation step of comparing the appearance frequency of a word or the probability of appearance of a word and determining a domain-specific score that is a score of how characteristic a word is in the corresponding word through the difference; The domain-specific term classification module, which is a component of the virtual label generation apparatus, classifies domain-specific terms, which are terms specific to the domain, among the phrases of the query data based on the domain-specific score calculated by the domain- Domain-specific term classification step; And a clustering step of clustering the query data into at least one similar sentence group based on the degree of similarity with the domain-specific terms classified by the domain-specific term classification module, the clustering module being a component of the virtual label generation apparatus Wherein the at least one similar phrase group generated by the clustering module is provided to the client of the chatbot manager so that the chatbot administrator can label the similar sentence group. And a method for generating a virtual label for the virtual label.

Another object of the present invention is to provide a virtual label generation method using a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot receiving question data and outputting specific answer data, A classifying module which is a constituent unit of the apparatus receives the question data in a general string form and classifies the question data into entities or intents and processes the question data into vector values; The domain-specific score calculation module, which is a component of the virtual label generation device, calculates a word occurrence frequency or a word occurrence probability in the question data and a word occurrence probability in the general corpus in order to judge whether the word classified in the classification module is characteristic to a specific domain A domain-specific score calculation step of comparing the appearance frequency of a word or the probability of appearance of a word and determining a domain-specific score that is a score of how characteristic a word is in the corresponding word through the difference; Wherein the high frequency general term classification module which is a component of the virtual label generation apparatus comprises a domain specific word calculation module for calculating a domain specific word and a general term specific to the corresponding domain of the query data based on the domain specific word, A high frequency general term classification step of classifying a high frequency general term having a frequency of occurrence of a word or a probability of occurrence of a word among the general terms; And a clustering module for clustering the query data into at least one similar sentence group on the basis of the degree of similarity with the high frequency general term classified by the high frequency general term classification module, Wherein the at least one similar sentence group generated by the clustering module is provided to the client of the chatbot manager so that the chatbot administrator can label the similar sentence group, Can be achieved by providing a method of generating a virtual label for labeling.

Another object of the present invention is to provide a virtual label generation method using a virtual label generation apparatus for labeling a training set for learning of an artificial intelligence module of a chatbot receiving question data and outputting specific answer data, A classifying module which is a constituent unit of the apparatus receives the question data in a general string form and classifies the question data into entities or intents and processes the question data into vector values; The domain-specific score calculation module, which is a component of the virtual label generation device, calculates a word occurrence frequency or a word occurrence probability in the question data and a word occurrence probability in the general corpus in order to judge whether the word classified in the classification module is characteristic to a specific domain A domain-specific score calculation step of comparing the appearance frequency of a word or the probability of appearance of a word and determining a domain-specific score that is a score of how characteristic a word is in the corresponding word through the difference; Wherein the high frequency general term classification module which is a component of the virtual label generation apparatus comprises a domain specific word calculation module for calculating a domain specific word and a general term specific to the corresponding domain of the query data based on the domain specific word, A high frequency general term classification step of classifying a high frequency general term having a frequency of occurrence of a word or a probability of occurrence of a word among the general terms; A clustering step of clustering the query data into at least one similar sentence group on the basis of the degree of similarity with the high frequency general term classified by the high frequency general term classification module; Wherein the second classifying module, which is a component of the virtual label generating apparatus, searches a plurality of sentences included in the similar sentence group clustered on the basis of the similarity with the high frequency general term in the clustering module, A second classifying step of classifying the sentence into at least one domain similar sentence group; Wherein the domain diversity calculation module, which is a constitution of the virtual label generation apparatus, calculates a domain diversity of the similar sentence group based on the number of the domain similar sentence groups classified in the similar sentence group by the second classification module, Diversity calculation step; And a label sentence group determination module that is a component of the virtual label generation device determines the importance of the similar sentence group based on the domain diversity of the similar sentence group calculated by the domain diversity calculation module and provides the similar sentence group to the chatbox manager And a label sentence group determination step of determining a label sentence group that is a sentence group, wherein the label sentence group is provided to a client of the chatbot manager so that the chatbot manager can label the label sentence group. This can be achieved by providing a virtual label generation method for labeling a training set of a chatbot.

It is another object of the present invention to provide a classifying module for classifying question data of a general string form into entities or intents; In order to judge whether or not the segmented phrases classified in the classification module are characteristic to a specific domain, the probability of occurrence of a word or occurrence of a word in the question data is compared with the probability of occurrence of a word or occurrence of a word in a general corpus, A domain-specific score calculation module for determining a domain-specific score that is a score of how characteristic a phrase is in the domain; A domain-specific term classification module for classifying a domain-specific term, which is a term specific to the domain, among the phrases of the question data, based on the domain-specific score calculated by the domain-specific score calculation module; A high frequency general term classification module for classifying the domain-specific terms and general terms specific to the domain among the phrases of the question data and classifying high frequency common terms having a high frequency of occurrence of phrases or phrases among the general terms; And a clustering module for clustering the query data into at least one similar sentence group based on the degree of similarity with the domain-specific term and the high frequency general term classified by the domain-specific term classification module. Can be achieved by providing a sentence clustering apparatus using high frequency general terms.

Another object of the present invention is to provide a classifying module for classifying question data of a general string type into entities or intents; The domain-specific score calculation module calculates the probability of occurrence of a word in the question data or the probability of occurrence of a word in the question data and the probability of occurrence of a word in the general corpus or the probability of occurrence of a word in the general corpus, A domain-specific score calculation step of determining a domain-specific score, which is a score of a characteristic word for the corresponding word, through a difference; Wherein the domain-specific term classification module classifies domain-specific terms, which are terms specific to the domain, among the phrases of the query data based on the domain-specific score calculated by the domain-specific score calculation module; Wherein the high frequency general term classification module classifies the domain-specific terms and general terms specific to the domain among the phrases of the question data and classifies high frequency general terms having a high frequency of occurrence of phrases or phrases High frequency general term classification step; And clustering module clustering the query data into at least one similar sentence group based on the degree of similarity with the domain-specific term and the high frequency general term classified by the domain-specific term classification module. Domain specific terms and high frequency general terms.

Another object of the present invention is to provide a classifying module for classifying question data of a general string type into entities or intents; The domain-specific score calculation module calculates the probability of occurrence of a word in the question data or the probability of occurrence of a word in the question data and the probability of occurrence of a word in the general corpus or the probability of occurrence of a word in the general corpus, A domain-specific score calculation step of determining a domain-specific score, which is a score of a characteristic word for the corresponding word, through a difference; Wherein the domain-specific term classification module classifies domain-specific terms, which are terms specific to the domain, among the phrases of the query data based on the domain-specific score calculated by the domain-specific score calculation module; Wherein the high frequency general term classification module classifies the domain-specific terms and general terms specific to the domain among the phrases of the question data and classifies high frequency general terms having a high frequency of occurrence of phrases or phrases High frequency general term classification step; And clustering module clustering the query data into at least one similar sentence group based on the degree of similarity with the domain-specific term and the high frequency general term classified by the domain-specific term classification module. It is possible to achieve a sentence clustering method using a domain specific term and a high frequency general term by providing a program stored in a recording medium which is executed on a computer.

As described above, the present invention has the following effects.

First, according to one embodiment of the present invention, if the existing 100,000 data sets are grouped into ten labels, the label required for map learning can be ended by inputting ten answers. The criteria that are classified in this process can generate valid training data that can be more clearly distinguished than the manually assigned data because labels are assigned in a methodology that is similar to the criteria for learning maps.

Second, according to one embodiment of the present invention, when the question data is not processed and the label itself is not formed, or when the label is erroneously formed (i.e., the matching of the question and the answer is wrong) The effect of clustering the sentences of the question data related to each other is generated.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and, together with the description, And shall not be interpreted.
1 is a schematic diagram showing a basic module configuration of a chatbox,
FIG. 2 is a schematic diagram specifically showing a general chatbot module configuration,
FIG. 3 is a schematic diagram specifically showing the configuration of a chatbot module using machine learning,
FIG. 4 is a schematic diagram illustrating a relationship between a virtual label generation apparatus and a chatbox AI module according to an embodiment of the present invention;
5 is a schematic diagram illustrating a virtual label generation apparatus according to a first embodiment of the present invention.
6 is a schematic diagram showing a virtual label generating apparatus according to a second embodiment of the present invention.
FIG. 7 illustrates classification of query data by the high frequency general term classification module according to an embodiment of the present invention,
FIG. 8 is a schematic diagram showing a virtual label generation apparatus according to a third embodiment of the present invention,
9 is a schematic diagram showing an example of classification into a domain similar sentence group by the secondary classification module 41 according to the third embodiment of the present invention,
FIG. 10 is a schematic diagram illustrating a method of generating a virtual label according to the first embodiment of the present invention. FIG.
11 is a diagram illustrating an example of a result of each step of the method of generating a virtual label according to the first embodiment of the present invention,
12 is a schematic diagram illustrating a method of generating a virtual label according to a second embodiment of the present invention.
13 is a diagram illustrating an example of a result of each step of the method of generating a virtual label according to the second embodiment of the present invention,
FIG. 14 is a schematic diagram illustrating a method of generating a virtual label according to a third embodiment of the present invention;
FIG. 15 is a diagram illustrating an example of a result of each step of the method of generating a virtual label according to the third embodiment of the present invention,
FIG. 16 illustrates a method of generating a virtual label according to a fourth embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following detailed description of the operation principle of the preferred embodiment of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may unnecessarily obscure the subject matter of the present invention.

The same reference numerals are used for portions having similar functions and functions throughout the drawings. In the specification, when a specific portion is connected to another portion, it includes not only a direct connection but also a case where the other portion is indirectly connected with another element in between. In addition, the inclusion of a specific constituent element does not exclude other constituent elements unless specifically stated otherwise, but may include other constituent elements.

Virtual Label Generator for Chatset's Training Set Labeling for Specific Domains

4 is a schematic diagram illustrating a relationship between a virtual label generation apparatus and a chatbox AI module according to an embodiment of the present invention. 4, the virtual label generation apparatus 1 according to the embodiment of the present invention receives inquiry data 200, question query data inputted by a user or existing Q & A data set provided by a client company, And generates a training set to be applied to the learning of the chatbox AI module 10. The query data 200 may include sentences or unlabeled sentences of the query labeled to be mapped to a particular answer. According to the virtual label generating apparatus 1 according to the embodiment of the present invention, the chatbot AI module 10 can implement a chatbot characteristic of a specific domain even with a wrong label or an unambiguous query data set do.

[First Embodiment]

5 is a schematic diagram showing a virtual label generating apparatus according to a first embodiment of the present invention. 5, the virtual label generating apparatus 1 according to the first embodiment of the present invention can input question data 200 and can include a classification module 2, a classification module 3, (5).

The class module 2 receives the question data 200 as a normal text form and inputs the general string form into the NLP module 21 as an entity or an intent It is a module to distinguish by the phrase. The NLP module 21 may specifically include morphological analysis, stem extraction, abstraction extraction, TF, TFIDF, and the like. Then, the vector values are used to process a phrase such as an entity or an intent separated by the vectorization module 22 (Sentence2vec, Word2vec, SyntexNet). Or Word2vec may be used, and specifically, an n-gram, a CBOW model for predicting a word from a context, or a Skip-gram model for predicting a context from a word may be used.

The classification module 3 is a module for clustering sentences of the question data 200 based on a specific rule according to an embodiment of the present invention and generating a similar sentence group which is an unlabeled group to be. The classification module 3 according to an embodiment of the present invention may include a domain-specific score calculation module 31, a domain-specific term classification module 32, and a clustering module 33.

The domain-specific score calculation module 31 calculates the frequency of occurrence of a word (or the probability of occurrence of a word) in question data and the frequency of occurrence of a word in a general corpus in order to judge whether or not the word classified in the classification module 2 is characteristic to a specific domain (Or probability of occurrence of a word), and determines a domain-specific score, which is a score of how characteristic a phrase is in the corresponding domain, through a difference of two frequencies. (Or probabilities of occurrence of words) in the general corpus by checking the left and right words of each word in the general corpus, and comparing the occurrence frequency of the words in the question data (200) with the frequency of occurrence of the words in the general corpus (Or probability of appearance of a word) is higher, the phrase may be determined to be characteristic to the corresponding domain. Depending on the amount of data in the general corpus, we can increase or decrease the distance of the correlation, which will be the basis for determining the frequency of occurrence of the word, to i + n, i-n. Here, i is the index of the word in the sentence. The amount of data and the constant n can be proportional.

The domain-specific term classification module 32 classifies domain-specific terms among the phrases of the question data 200 based on the domain-specific score calculated by the domain-specific score calculation module 31. The words of the question data 200 by the domain-specific term classification module 32 can be divided into domain-specific terms and general terms.

The clustering module 33 compares the sentence of the question data 200 including phrases (similar phrases) close to each domain-specific term classified by the domain-specific term classification module 32 (similarity phrase) (Similar sentence group). The degree of similarity with domain-specific terms can be determined by using the similarity using General Corpus. This is called Corpus-used Similarity. For example, in the case of Korean, the National Corpus of General Corpus can be used, and English can use Brown Corpus. Alternatively, a similarity measure using a semantic network can be used. This is called Knowledge-based Similarity. For example, wordNet. The similarity measure between word and word using wordNet is based on the similarity of path, Leacock & Chodorow Similarity, which utilizes minimum distance and maximum depth of node, Wu & Palmer Similarity Or the like.

The labeling module 5 provides at least one similar sentence group (composed of a plurality of sentences) generated by the clustering module 33 to the clients of the chatbot manager, and corresponds to at least one similar sentence group in the client of the chatbot manager And a question and answer are matched. An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5.

[Second Embodiment]

6 is a schematic diagram showing a virtual label generation apparatus according to a second embodiment of the present invention. 6, the virtual label generating apparatus 1 according to the second embodiment of the present invention can input query data 200 and include a classification module 2, a classification module 3, a labeling module 3, (5).

The class module 2 receives the question data 200 as a normal text form and inputs the general string form into the NLP module 21 as an entity or an intent It is a module to distinguish by the phrase. The NLP module 21 may specifically include morphological analysis, stem extraction, abstraction extraction, TF, TFIDF, and the like. Then, the vector values are used to process a phrase such as an entity or an intent separated by the vectorization module 22 (Sentence2vec, Word2vec, SyntexNet). Or Word2vec may be used, and specifically, an n-gram, a CBOW model for predicting a word from a context, or a Skip-gram model for predicting a context from a word may be used.

The classification module 3 is a module for clustering sentences of the question data 200 based on a specific rule according to an embodiment of the present invention and generating a similar sentence group which is an unlabeled group to be. The classification module 3 according to an embodiment of the present invention may include a domain-specific score calculation module 31, a high frequency general term classification module 34, and a clustering module 33.

The domain-specific score calculation module 31 calculates the frequency of occurrence of a word (or the probability of occurrence of a word) in question data and the frequency of occurrence of a word in a general corpus in order to judge whether or not the word classified in the classification module 2 is characteristic to a specific domain (Or probability of occurrence of a word), and determines a domain-specific score, which is a score of how characteristic a phrase is in the corresponding domain, through a difference of two frequencies. (Or probabilities of occurrence of words) in the general corpus by checking the left and right words of each word in the general corpus, and comparing the occurrence frequency of the words in the question data (200) with the frequency of occurrence of the words in the general corpus (Or probability of appearance of a word) is higher, the phrase may be determined to be characteristic to the corresponding domain. Depending on the amount of data in the general corpus, we can increase or decrease the distance of the correlation, which will be the basis for determining the frequency of occurrence of the word, to i + n, i-n. Here, i is the index of the word in the sentence. The amount of data and the constant n can be proportional.

The high frequency general term classification module 34 classifies the high frequency general terms among the phrases of the question data 200 based on the domain specific score calculated by the domain specific score calculation module 31. [ FIG. 7 illustrates classification of query data by the high frequency general term classification module according to an embodiment of the present invention. As shown in FIG. 7, the phrases of the question data 200 by the high frequency general term classification module 34 can be divided into domain-specific terms and general terms.

General terms can be divided into high frequency general terms and low frequency general terms. The domain-specific term according to an embodiment of the present invention is defined as a domain specific score of a query corpus, as defined in the domain-specific score calculation module 31, It means a much higher phrase. The rest of the phrases that are not domain specific terms can be classified into general terms, and general terms can be classified into high frequency general terms and low frequency general terms. The high frequency general term has a high probability frequency in the domain but a high probability frequency in the general corpus so that the frequency of appearance probability of the phrase does not greatly differ between the question data 200 and the general corpus, . The reference point of words that are high frequency general terms may be defined as terms higher than the frequency of domain specific terms having the lowest frequency, for example. High frequency general terms may include many commonly used expressions - place (local name, local pronoun), time (tomorrow, morning, next week, etc.) - and general terms that have no specific meaning in the domain It can be confirmed based on the appearance frequency in the domain.

Low frequency generic terms can mean generic terms except high frequency generic terms. That is, the low frequency general term may mean that the occurrence probability frequency is low in the question data 200 and the occurrence probability frequency is low in the general corpus. The low frequency general term may be determined to be close to noise in the clustering of the query data 200. [

The clustering module 33 includes at least one sentence of query data 200 that includes a close proximity (similarity) phrase (similar phrase) to each high frequency general term classified by the high frequency common term classification module 34 (Similar sentence group). The degree of similarity with high frequency general terms can be determined by using the similarity using General Corpus. This is called Corpus-used Similarity. For example, in the case of Korean, the National Corpus of General Corpus can be used, and English can use Brown Corpus. Alternatively, a similarity measure using a semantic network can be used. This is called Knowledge-based Similarity. For example, wordNet. The similarity measure between word and word using wordNet is based on the similarity of path, Leacock & Chodorow Similarity, which utilizes minimum distance and maximum depth of node, Wu & Palmer Similarity Or the like.

The labeling module 5 provides at least one similar sentence group (composed of a plurality of sentences) generated by the clustering module 33 to the clients of the chatbot manager, and corresponds to at least one similar sentence group in the client of the chatbot manager And a question and answer are matched. An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5.

[Third Embodiment]

8 is a schematic diagram showing a virtual label generation apparatus according to a third embodiment of the present invention. 8, the virtual label generating apparatus 1 according to the third embodiment of the present invention can input question data 200, and can be classified into a classification module 2, a classification module 3, A module 4, and a labeling module 5.

The class module 2 receives the question data 200 as a normal text form and inputs the general string form into the NLP module 21 as an entity or an intent It is a module to distinguish by the phrase. The NLP module 21 may specifically include morphological analysis, stem extraction, abstraction extraction, TF, TFIDF, and the like. Then, the vector values are used to process a phrase such as an entity or an intent separated by the vectorization module 22 (Sentence2vec, Word2vec, SyntexNet). Or Word2vec may be used, and specifically, an n-gram, a CBOW model for predicting a word from a context, or a Skip-gram model for predicting a context from a word may be used.

The classification module 3 is a module for clustering sentences of the question data 200 based on a specific rule according to an embodiment of the present invention and generating a similar sentence group which is an unlabeled group to be. The classification module 3 according to an embodiment of the present invention may include a domain-specific score calculation module 31, a high frequency general term classification module 34, and a clustering module 33.

The domain-specific score calculation module 31 calculates the frequency of occurrence of a word (or the probability of occurrence of a word) in question data and the frequency of occurrence of a word in a general corpus in order to judge whether or not the word classified in the classification module 2 is characteristic to a specific domain (Or probability of occurrence of a word), and determines a domain-specific score, which is a score of how characteristic a phrase is in the corresponding domain, through a difference of two frequencies. (Or probabilities of occurrence of words) in the general corpus by checking the left and right words of each word in the general corpus, and comparing the occurrence frequency of the words in the question data (200) with the frequency of occurrence of the words in the general corpus (Or probability of appearance of a word) is higher, the phrase may be determined to be characteristic to the corresponding domain. Depending on the amount of data in the general corpus, we can increase or decrease the distance of the correlation, which will be the basis for determining the frequency of occurrence of the word, to i + n, i-n. Here, i is the index of the word in the sentence. The amount of data and the constant n can be proportional.

The high frequency general term classification module 34 classifies the high frequency general terms among the phrases of the question data 200 based on the domain specific score calculated by the domain specific score calculation module 31. [ FIG. 7 illustrates classification of query data by the high frequency general term classification module according to an embodiment of the present invention. As shown in FIG. 7, the phrases of the question data 200 by the high frequency general term classification module 34 can be divided into domain-specific terms and general terms.

General terms can be divided into high frequency general terms and low frequency general terms. The domain-specific term according to an embodiment of the present invention is defined as a domain specific score of a query corpus, as defined in the domain-specific score calculation module 31, It means a much higher phrase. The rest of the phrases that are not domain specific terms can be classified into general terms, and general terms can be classified into high frequency general terms and low frequency general terms. The high frequency general term has a high probability frequency in the domain but a high probability frequency in the general corpus so that the frequency of appearance probability of the phrase does not greatly differ between the question data 200 and the general corpus, . The reference point of words that are high frequency general terms may be defined as terms higher than the frequency of domain specific terms having the lowest frequency, for example. High frequency general terms may include many commonly used expressions - place (local name, local pronoun), time (tomorrow, morning, next week, etc.) - and general terms that have no specific meaning in the domain It can be confirmed based on the appearance frequency in the domain.

In the case of high frequency general terms, it is difficult to automate the meaning of the set of phrases by calculating the distance between each phrase from a general corpus and grouping the aggregated words at a close distance. However, If you look at the phrases, you can see that they are classified into sentence group which means time, and sentence group which means place. Accordingly, the high frequency generic term can be defined as a special or important term in the domain.

Low frequency generic terms can mean generic terms except high frequency generic terms. That is, the low frequency general term may mean that the occurrence probability frequency is low in the question data 200 and the occurrence probability frequency is low in the general corpus. The low frequency general term may be determined to be close to noise in the clustering of the query data 200. [

The clustering module 33 includes at least one sentence of query data 200 that includes a close proximity (similarity) phrase (similar phrase) to each high frequency general term classified by the high frequency common term classification module 34 (Similar sentence group). The degree of similarity with high frequency general terms can be determined by using the similarity using General Corpus. This is called Corpus-used Similarity. For example, in the case of Korean, the National Corpus of General Corpus can be used, and English can use Brown Corpus. Alternatively, a similarity measure using a semantic network can be used. This is called Knowledge-based Similarity. For example, wordNet. The similarity measure between word and word using wordNet is based on the similarity of path, Leacock & Chodorow Similarity, which utilizes minimum distance and maximum depth of node, Wu & Palmer Similarity Or the like.

As another example, when the sentence of question data itself is only domain-specific terms, clustering can be performed separately from clustering through similarity with high frequency general terms. The similar sentence group clustered in similarity with the domain-specific terms can be provided to the labeling module 5 directly without passing through the importance determination module 4. [

The importance level determination module 4 is a module for determining whether or not it is an important similar sentence group in the domain in at least one similar sentence group cluster (a sentence group including a phrase similar to the high frequency common term) clustered in the clustering module 33. [ The importance level determination module 4 may include a secondary classification module 41, a domain diversity calculation module 42, and a label sentence group determination module 43.

The second classification module 41 generates a plurality of sentences included in at least one similar sentence group clustered based on the similarity with the high frequency general term in the clustering module 33 based on the degree of similarity with the domain- And classify them into domain similar sentence groups. 9 is a schematic diagram showing an example of classification into a domain similar sentence group by the secondary classification module 41 according to the third embodiment of the present invention. As shown in FIG. 9, according to the second classification module 41 according to the third embodiment of the present invention, the A-like sentence group and the B-like sentence group are classified based on the high frequency general term, Each similar sentence group can be classified into at least one domain similar sentence group.

The domain diversity calculation module 42 is a module for calculating the domain diversity of the similar sentence group based on the distribution of at least one domain similar sentence group classified in the similar sentence group by the secondary classification module 41. As an example of calculating the domain diversity in accordance with the third embodiment of the present invention, it is possible to calculate how many of the total query data 200 are classified based on the degree of similarity with domain-specific terms, A method of comparing with the number of similar sentence groups can be used. For example, when the entire query data 200 can be classified into 10 groups by the degree of similarity with domain-specific terms, the A-like sentence group is classified into five domain-similar sentence groups, and the B- It can be determined that the domain diversity of the A similar sentence group is lower when it is classified into the domain similar sentence group.

The label sentence group judging module 43 judges the importance of a specific similar sentence group based on the domain diversity of a specific similar sentence group calculated by the domain diversity calculation module and determines a label sentence group as a sentence group to be. The label sentence group judging module 43 judges the importance of the group based on the domain diversity of the similar sentence group and provides the chatbot manager with the sentence group It is possible to determine whether the label is determined to be a label sentence group provided to the manager or a sentence group not provided to the chatbot manager.

For example, the importance level of the similar sentence group may be a criterion for determining whether or not to provide the chatbot administrator with the labeling module 5 of the similar sentence group. For example, the higher the domain diversity of a similar sentence group, the more likely it is that a sentence group does not affect the division of the label in the corresponding domain, and the lower the domain diversity of the similar sentence group, It can be determined that there is a high possibility that the sentence group is a sentence group. Sentence groups that affect label splitting can be defined as a group of label sentences and provided to the labeling module to provide to the chambot administrator. Sentence groups that do not affect label splitting can be defined as unlabeled sentence groups and provided to the labeling module not to be served to the chambot administrator. According to the third embodiment of the present invention, in the case of a similar sentence group having a high domain diversity, the domain similar sentence groups generated from the similar sentence group clustered in similarity with the corresponding high frequency general term are judged to be invalid, And can be configured to be absorbed in a similar sentence group composed of similarity with domain-specific terms.

As another example, in the case of a similar sentence group having a low domain diversity, a domain similar sentence group classified as a second order can be determined as a label sentence group. In the case of a similar sentence group having a high domain diversity, the similar sentence group itself can be determined as a label sentence group regardless of being classified as a second order, thereby reducing the burden on the chatbot manager. In this case, the domain diversity that is the importance of the similar sentence group is a criterion for determining whether the similar sentence group is a label sentence group or whether each domain similar sentence group classified in the similar sentence group is a label sentence group .

The labeling module 5 sends a similar sentence group (label sentence group) determined to be provided by the label sentence group determination module 43 to at least one of the at least one similar sentence group generated by the clustering module 33, And the client of the chatbot manager receives the label corresponding to the similar sentence group and matches the question and the answer.

An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5. The redundant group of contexts may be reduced through the domain similar sentence group distribution rate within the similar sentence group clustered in similarity with the high frequency general term in the importance level determination module 4. For example, if the question data is classified into only the degree of similarity with the domain-specific term or the degree of similarity with the high-frequency general term, if the chatbot manager has to perform 60 labels, it is not important to perform labeling in accordance with the third embodiment of the present invention The similar sentence groups can be excluded, so that only 20 labeling operations can be performed.

As another example, when the sentence of the question data itself is only a domain-specific term, the clustering module 33 can perform clustering separately from the cluster based on the degree of similarity with the high-frequency general term. The similar sentence group clustered in similarity with the domain-specific terms can be provided to the labeling module 5 directly without passing through the importance determination module 4. [ In this case, the labeling module 5 can provide a client of the chatbot manager with a similar sentence group composed only of domain-specific terms and a label sentence group determined by the label sentence group determination module 43 to provide to the chatbot manager.

How to create a virtual label for chattob's training set labeling for a specific domain

[First Embodiment]

10 is a schematic diagram showing a method of generating a virtual label according to the first embodiment of the present invention. 10, the method of generating a virtual label according to the first embodiment of the present invention includes a classification step S10, a domain-specialized score generation step S11, a domain-specific term classification step S12, a clustering step S13 ).

In the discriminating step S10, the discrimination module 2 receives the question data 200 as a normal text form, and the general character string type thus inputted is inputted to the NLP module 21 as an entity And a phrase such as a semantic intent, and converting it into a vector value by the vectorization module 22. The NLP module 21 can segment the morpheme with functions such as morphological analysis, stem extraction, abstraction extraction, TF, and TFIDF. The vectorization module 22 (Sentence2vec, Word2vec, and SyntexNet) treats words such as a separated entity or an intent as vector values. Word2vec can be used, specifically, n-gram, context A CBOW model for predicting a word from a word, or a Skip-gram model for predicting a context from a word can be used.

The domain-specific score generation step S11 is a step in which the domain-specific score calculation module 31, which is a component of the classification module 3, (Or probabilities of occurrence) in the general corpus are compared with the frequencies of occurrence of the words in the general corpus (or the probabilities of occurrence of the words in the general corpus), and the score of the characteristic word Domain-specific score. (Or probabilities of occurrence of words) in the general corpus by checking the left and right words of each word in the general corpus, and comparing the occurrence frequency of the words in the question data (200) with the frequency of occurrence of the words in the general corpus (Or probability of appearance of a word) is higher, the phrase may be determined to be characteristic to the corresponding domain. Depending on the amount of data in the general corpus, we can increase or decrease the distance of the correlation, which will be the basis for determining the frequency of occurrence of the word, to i + n, i-n. Here, i is the index of the word in the sentence. The amount of data and the constant n can be proportional.

The domain-specific term classification step S12 is a step in which the domain-specific term classification module 32, which is a constituent of the classification module 3, calculates the number of the question data 200 based on the domain- It is a step to classify domain-specific terms among the phrases. The words of the question data 200 by the domain-specific term classification module 32 can be divided into domain-specific terms and general terms.

The clustering step S13 is a step in which the clustering module 33 which is a constituent of the classification module 3 searches for a phrase of a close distance (similarity) to each domain-specific term classified by the domain- ) Is clustered into at least one group (similar sentence group). The degree of similarity with domain-specific terms can be determined by using the similarity using General Corpus. This is called Corpus-used Similarity. For example, in the case of Korean, the National Corpus of General Corpus can be used, and English can use Brown Corpus. Alternatively, a similarity measure using a semantic network can be used. This is called Knowledge-based Similarity. For example, wordNet. The similarity measure between word and word using wordNet is based on the similarity of path, Leacock & Chodorow Similarity, which utilizes minimum distance and maximum depth of node, Wu & Palmer Similarity Or the like.

The at least one similar sentence group generated in the clustering step S13 is provided to the client of the chatbot manager by the labeling module 5 and receives a label corresponding to at least one similar sentence group from the client of the chatbot manager, And the answer is matched. An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5.

11 is a diagram illustrating an example of a result of each step of the method of generating a virtual label according to the first embodiment of the present invention. 11, in S10, the question data is segmented into morphemes by morphological analysis as 'seasoned chicken', 'order', 'delivery', 'Gangnam Station', 'Jamsil Station', 'morning' and 'afternoon' , 'Order' = 0.8, 'Delivery' = 0.7, 'Gangnam Station' = 0.2, 'Jamsil Station' = 0.2, which is the score of domain specificity score, , 'Morning' = 0.1, and 'afternoon' = 0.1. The higher the domain-specific score, the higher the probability of appearance in question data than in general corpus. In S12, the phrases are classified into domain specific terms ('spicy chicken', 'order', 'delivery') and general terms ('Kangnam Station', 'Jamsil Station', 'AM', 'afternoon' . At step S13, at least one similar sentence group (1 group: 'can be delivered in the morning', 'can be ordered in the afternoon', 'can be delivered to Jamsil station') is generated based on the degree of similarity with the domain- The group is provided to the chambot administrator in the labeling module, and after tagging by the administrator, it is used as the training set of the chatbox AI module.

According to the first embodiment of the present invention, it is possible to perform clustering more specific to a domain than to clustering question data using a general corpus. In addition, it is possible to solve the problem of preparing and solving a dictionary without a separate dictionary, thereby resulting in a significant cost and a reduction in human resources.

[Second Embodiment]

12 is a schematic diagram showing a method of generating a virtual label according to a second embodiment of the present invention. 12, a method of generating a virtual label according to a second embodiment of the present invention includes a classification step S10, a domain-specialized score generation step S11, a high frequency general term classification step S12 ' (S13 ').

In the discriminating step S10, the discrimination module 2 receives the question data 200 as a normal text form, and the general character string type thus inputted is inputted to the NLP module 21 as an entity And a phrase such as a semantic intent, and converting it into a vector value by the vectorization module 22. The NLP module 21 can segment the morpheme with functions such as morphological analysis, stem extraction, abstraction extraction, TF, and TFIDF. The vectorization module 22 (Sentence2vec, Word2vec, and SyntexNet) treats words such as a separated entity or an intent as vector values. Word2vec can be used, specifically, n-gram, context A CBOW model for predicting a word from a word, or a Skip-gram model for predicting a context from a word can be used.

The domain-specific score generation step S11 is a step in which the domain-specific score calculation module 31, which is a component of the classification module 3, (Or probabilities of occurrence) in the general corpus are compared with the frequencies of occurrence of the words in the general corpus (or the probabilities of occurrence of the words in the general corpus), and the score of the characteristic word Domain-specific score. (Or probabilities of occurrence of words) in the general corpus by checking the left and right words of each word in the general corpus, and comparing the occurrence frequency of the words in the question data (200) with the frequency of occurrence of the words in the general corpus (Or probability of appearance of a word) is higher, the phrase may be determined to be characteristic to the corresponding domain. Depending on the amount of data in the general corpus, we can increase or decrease the distance of the correlation, which will be the basis for determining the frequency of occurrence of the word, to i + n, i-n. Here, i is the index of the word in the sentence. The amount of data and the constant n can be proportional.

The high frequency general term classification step S12 'is a step in which the high frequency general term classification module 34, which is a component of the classification module 3, 200) is a step of sorting high frequency common terms among the phrases. The phrases of the query data 200 by the high frequency general term classification module 34 can be divided into domain-specific terms and general terms.

General terms can be divided into high frequency general terms and low frequency general terms. The domain-specific term according to an embodiment of the present invention is defined as a domain specific score of a query corpus, as defined in the domain-specific score calculation module 31, It means a much higher phrase. The rest of the phrases that are not domain specific terms can be classified into general terms, and general terms can be classified into high frequency general terms and low frequency general terms. The high frequency general term has a high probability frequency in the domain but a high probability frequency in the general corpus so that the frequency of appearance probability of the phrase does not greatly differ between the question data 200 and the general corpus, . The reference point of words that are high frequency general terms may be defined as terms higher than the frequency of domain specific terms having the lowest frequency, for example. High frequency general terms may include many commonly used expressions - place (local name, local pronoun), time (tomorrow, morning, next week, etc.) - and general terms that have no specific meaning in the domain It can be confirmed based on the appearance frequency in the domain.

Low frequency generic terms can mean generic terms except high frequency generic terms. That is, the low frequency general term may mean that the occurrence probability frequency is low in the question data 200 and the occurrence probability frequency is low in the general corpus. The low frequency general term may be determined to be close to noise in the clustering of the query data 200. [

The clustering step S13 'is a step in which the clustering module 33, which is a constituent of the classification module 3, searches for a phrase having a close distance (similarity) to each high frequency general term classified by the high frequency general term classification module 34 (Similar phrases) are clustered into at least one or more groups (similar sentence groups). The degree of similarity with high frequency general terms can be determined by using the similarity using General Corpus. This is called Corpus-used Similarity. For example, in the case of Korean, the National Corpus of General Corpus can be used, and English can use Brown Corpus. Alternatively, a similarity measure using a semantic network can be used. This is called Knowledge-based Similarity. For example, wordNet. The similarity measure between word and word using wordNet is based on the similarity of path, Leacock & Chodorow Similarity, which utilizes minimum distance and maximum depth of node, Wu & Palmer Similarity Or the like.

At least one similar sentence group generated in the clustering step S13 'is provided to the client of the chatbot manager by the labeling module 5, and receives a label corresponding to at least one similar sentence group from the client of the chatbot manager Matching questions and answers. An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5.

FIG. 13 shows an example of the result of each step of the method of generating a virtual label according to the second embodiment of the present invention. According to FIG. 13, in S10, the question data is segmented into morphemes by morphological analysis as 'seasoned chicken', 'order', 'delivery', 'Gangnam Station', 'Jamsil Station', 'morning' and 'afternoon' , 'Order' = 0.8, 'Delivery' = 0.7, 'Gangnam Station' = 0.2, 'Jamsil Station' = 0.2, which is the score of domain specificity score, , 'Morning' = 0.1, and 'afternoon' = 0.1. The higher the domain-specific score, the higher the probability of appearance in question data than in general corpus. S12 ', the corresponding phrases are classified into domain specific terms (' spicy chicken ',' order ',' delivery '), high frequency general terms (' am 'and' afternoon ') and low frequency general terms Gangnam Station ',' Jamsil Station '). S13 'generates at least one similar sentence group (1 group:' Can it be delivered in the morning ',' Can it be ordered in the afternoon ',' Can it be delivered to Jamsil station tomorrow afternoon ') based on the similarity with the high frequency general term? These similar sentence groups are provided to the chatbot manager in the labeling module, and after tagging by the manager, they are used as a training set of the chatbot artificial intelligence module.

According to the second embodiment of the present invention, it is possible to perform domain-specific clustering much more than cluster the question data using a general corpus. In particular, the use of similarity with domain-specific terms makes it possible to cluster clusters of high-frequency generic terms in real domains that may be missed when clustering is performed. In addition, it is possible to solve the problem of preparing and solving a dictionary without a separate dictionary, thereby resulting in a significant cost and a reduction in human resources.

[Third Embodiment]

FIG. 14 is a schematic diagram showing a method of generating a virtual label according to a third embodiment of the present invention, in relation to another embodiment of the present invention. 14, the method for generating a virtual label according to the third embodiment of the present invention includes a classification step S10, a domain-specialized score generation step S11, a high frequency general term classification step S12 ' (S13 '), a secondary classification step (S14), a domain diversity calculation step (S15), and a label sentence group determination step (S16).

The method of generating a virtual label according to the second embodiment of the present invention as described above may further comprise: a classification step S10, a domain-specific score generation step S11, a high frequency general term classification step S12 ', and a clustering step S13' same.

The secondary classification step S14 is a step of classifying the plurality of sentences included in at least one similar sentence group clustered on the basis of the degree of similarity with the high frequency general term in the clustering module 33, And classification into a domain similar sentence group by secondary classification based on similarity with terms.

The domain diversity calculation step S15 is a step in which the domain diversity calculation module 42 compares the domain of the similar sentence group 41 based on the distribution of at least one domain similar sentence group classified in the similar sentence group by the secondary classification module 41 It is a step to calculate diversity. As an example of calculating the domain diversity in accordance with the third embodiment of the present invention, it is possible to calculate how many of the total query data 200 are classified based on the degree of similarity with domain-specific terms, A method of comparing with the number of similar sentence groups can be used. For example, when the entire query data 200 can be classified into 10 groups by the degree of similarity with domain-specific terms, the A-like sentence group is classified into five domain-similar sentence groups, and the B- It can be determined that the domain diversity of the A similar sentence group is lower when it is classified into the domain similar sentence group.

In the label sentence group determination step S16, the label sentence group determination module 43 determines the importance of a specific similar sentence group based on the domain diversity of the specific similar sentence group calculated in the domain diversity calculation module, And determining a label sentence group that is a sentence group. The label sentence group judging module 43 judges the importance of the group based on the domain diversity of the similar sentence group and provides the chatbot manager with the sentence group It is possible to determine whether the label is determined to be a label sentence group provided to the manager or a sentence group not provided to the chatbot manager.

For example, the importance level of the similar sentence group may be a criterion for determining whether or not to provide the chatbot administrator with the labeling module 5 of the similar sentence group. For example, the higher the domain diversity of a similar sentence group, the more likely it is that a sentence group does not affect the division of the label in the corresponding domain, and the lower the domain diversity of the similar sentence group, It can be determined that there is a high possibility that the sentence group is a sentence group. Sentence groups that affect label splitting can be defined as a group of label sentences and provided to the labeling module to provide to the chambot administrator. Sentence groups that do not affect label splitting can be defined as unlabeled sentence groups and provided to the labeling module not to be served to the chambot administrator. According to the third embodiment of the present invention, in the case of a similar sentence group having a high domain diversity, the domain similar sentence groups generated from the similar sentence group clustered in similarity with the corresponding high frequency general term are judged to be invalid, And can be configured to be absorbed in a similar sentence group composed of similarity with domain-specific terms.

As another example, in the case of a similar sentence group having a low domain diversity, a domain similar sentence group classified as a second order can be determined as a label sentence group. In the case of a similar sentence group having a high domain diversity, the similar sentence group itself can be determined as a label sentence group regardless of being classified as a second order, thereby reducing the burden on the chatbot manager. In this case, the domain diversity that is the importance of the similar sentence group is a criterion for determining whether the similar sentence group is a label sentence group or whether each domain similar sentence group classified in the similar sentence group is a label sentence group .

At least one or more label sentence groups generated in the label sentence group determination step (S16) are provided to the client of the chattube manager by the labeling module (5), and a label corresponding to at least one or more label sentence groups It receives input and matches the question and answer. An effect that the question data as a highly accurate training set is completed is generated by the labeling module 5.

FIG. 15 illustrates an example of a result of each step of the method of generating a virtual label according to the third embodiment of the present invention. 15, in S10, the question data is segmented into morphemes by morphological analysis as 'seasoned chicken', 'order', 'delivery', 'Gangnam Station', 'Jamsil Station', 'morning', and 'afternoon' , 'Order' = 0.8, 'Delivery' = 0.7, 'Gangnam Station' = 0.2, 'Jamsil Station' = 0.2, which is the score of domain specificity score, , 'Morning' = 0.1, and 'afternoon' = 0.1. The higher the domain-specific score, the higher the probability of appearance in question data than in general corpus. S12 ', the corresponding phrases are classified into domain specific terms (' seasoned chicken ',' order ',' delivery ') and high frequency general terms (' morning ',' afternoon ',' ) And low frequency general terms ('cookies', 'refrigerators'). In S13 ', at least one similar sentence group (1 group:' can be delivered in the morning ',' can be ordered in the afternoon ',' can it be delivered to Jamsil station tomorrow afternoon ' : 'Can I order spicy chicken with Gangnam Station ' and 'Can it be delivered to Jamsil Station ') is generated. S14 in each of the similar statements secondary classification of sentences within the group, based on the similarity of the domain-specific terminology and domain similar sentence group (group 1 within group A: 'Can I deliver in the morning', 'Order possible in the afternoon,' 'Tomorrow Can be delivered to Jamsil Station in the afternoon ...?, B group within 1 group: Generate 'Can I have chicken in the morning ', 'Can I order lunch menu in the afternoon ', 'Can I fry tomorrow afternoon ' ...). In S15, the domain diversity (1 group = domain diversity 0.8, 2 groups = domain diversity 0.2, ...) of the similar sentence group is calculated based on the number of generated domain similar sentence groups. In step S16, a label sentence group, which is a sentence group to be provided to the chatbot manager, is determined. Based on the domain diversity, a certain sentence group is determined as a label sentence group. If domain diversity is high, it can be decided as a sentence group that is not important for labeling within the domain, and it can be determined as a label sentence group by not determining it as a label sentence group or incorporating a similar sentence group itself into a similar sentence group composed of domain- have. If the domain diversity is low, it may be determined to be a sentence group important for labeling within the domain, and at least a part of the domain similar sentence group may be determined as a label sentence group. Accordingly, the chambot administrator only has to label the important sentence group for labeling, so that the consumption of manpower is reduced.

According to the third embodiment of the present invention, clustering specialized for a domain can be performed much more than clustering question data using a general corpus. In particular, the use of similarity with domain-specific terms makes it possible to cluster clusters of high-frequency generic terms in real domains that may be missed when clustering is performed. In addition, it has the effect of automatically distinguishing between high-frequency generic terms that are important for distinguishing labels in domains and non-critical high-frequency generic terms. This enables domain-specific labeling. In addition, it is possible to solve the problem of preparing and solving a dictionary without a separate dictionary, thereby resulting in a significant cost and a reduction in human resources.

[Fourth Embodiment]

In another example, FIG. 16 shows a method of generating a virtual label according to the fourth embodiment of the present invention. As shown in FIG. 16, in the method of generating a virtual label according to the fourth embodiment of the present invention, the clustering method may be different depending on whether the sentence itself is only a domain-specific term, and branching step S121 .

According to the fourth embodiment of the present invention, when the sentence of the question data itself is only a domain-specific term, clustering can be performed based on the degree of similarity with the domain-specific term in addition to being clustered through the degree of similarity with the high- (S13). The similar sentence group clustered in similarity with the domain-specific terms can be provided to the labeling module 5 directly without passing through the importance determination module 4. [ In this case, the labeling module 5 can provide a client of the chatbot manager with a similar sentence group composed only of domain-specific terms and a label sentence group determined by the label sentence group determination module 43 to provide to the chatbot manager.

According to the fourth embodiment of the present invention, the steps S13 ', S14, S15, and S16 according to the third embodiment may be sequentially performed when the sentence of the question data is not limited to domain-specific terms.

According to the fourth embodiment of the present invention, a similar sentence group generated by S13 and a label sentence group generated by S16 can be provided to the chatbot administrator. If the domain similar sentence group is commonly used in the similar sentence group generated in S15, it is determined that the high frequency common term does not play an important role in the correlation between the labels and processed as noise, and the domain- The effect of which can be absorbed in a similar sentence group composed of only one sentence.

Although the chatbot has been described above for the convenience of explanation, the scope of the present invention is not limited to the chatbot, but an artificial intelligence module for voice analysis (voice bots), an artificial intelligence module for natural language analysis, Artificial intelligence module and the like may be included in the scope of the present invention.

As described above, those skilled in the art will appreciate that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the above-described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the present invention is defined by the appended claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention.

The features and advantages described herein are not all inclusive, and in particular, many additional features and advantages will be apparent to those skilled in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used herein is primarily chosen for readability and for purposes of teaching, and may not be selected to delineate or limit the subject matter of the invention.

The foregoing description of embodiments of the invention has been presented for purposes of illustration; It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Those skilled in the art will appreciate that many modifications and variations are possible in light of the above teachings.

Some portions of this description describe embodiments of the present invention relating to symbolic representations and algorithms of informational operations. These algorithmic descriptions and representations are generally used by those skilled in the data processing arts to efficiently convey the essence of their work to the other skilled artisan. While such operations are described functionally, computationally, or logically, they are understood to be implemented by a computer or equivalent electrical circuitry, microcode, or the like. Furthermore, this is also conveniently demonstrated without loss of generality, sometimes to represent an arrangement of such operations as a module. The operations described above and their associated modules may be implemented in software, firmware, hardware, or any combination thereof.

Any of the steps, operations, or processes described herein may be performed or implemented in conjunction with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprised of a computer-readable medium comprising computer program code, and the computer program code is executable to perform any or all of the processes, steps, May be executed by a computer processor.

Furthermore, embodiments of the invention may relate to an apparatus for performing the operations herein. These devices may include a general-purpose computing device that may be specially constructed and / or selectively activated or reconfigured by a computer program stored within the computer for the required purpose. Such a computer program may be stored in any type of media suitable for storing computer readable storage media or type of instructions, and may be coupled to a computer system bus. Further, any computing system referred to herein may comprise a single processor, or it may be a structure employing a multiprocessor design for increased computing power.

Finally, the language used herein has been chosen primarily for readability and for purposes of teaching, and may not be selected to describe or limit the subject matter of the invention.

The scope of the invention is, therefore, not to be limited by the Detailed Description, but is to be defined by the claims of any application based thereon. Accordingly, the disclosure of embodiments of the invention is illustrative and not restrictive of the scope of the invention, which is set forth in the following claims.

1: Virtual label generation device
2: Separation module
3: Classification module
4: Priority module
5: Labeling module
10: Chatbot AI module
21: NLP module
22: vectorization module
31: Domain-specific score determination module
32: High frequency general term classification module
33: Clustering module
34: Domain-specific term classification module
41: Secondary classification module
42: Domain Diversity Calculation Module
43: label sentence group judgment module
200: Question Data

Claims (3)

A division module for receiving the question data in the form of a general string and dividing the question data into phrases;
In order to determine whether the word classified in the classification module is characteristic to a specific domain, the probability of occurrence of a word or occurrence of a word in the question data is compared with the probability of occurrence of a word or occurrence of a word in a general corpus, A domain-specific score calculation module for determining a domain-specific score, which is a score of a characteristic word of the corresponding phrase in the domain;
And classifying domain specific terms and general terms specific to the domain among the phrases of the question data based on the domain specific score calculated by the domain specific score calculation module, A high frequency general term classification module for classifying frequency generic terms;
A clustering module for clustering the query data into at least one similar sentence group based on the degree of similarity with the high frequency general term classified by the high frequency general term classification module;
The clustering module classifies a plurality of sentences included in the similar sentence group clustered based on the similarity with the high frequency general term on the basis of the degree of similarity with the domain specific word and classifies the sentence into at least one domain similar sentence group Classification module; And
A domain diversity calculation module for calculating a domain diversity of the similar sentence group based on the number of the domain similar sentence groups classified in the similar sentence group by the classification module;
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A domain diversity calculation device using domain - specific terms and high frequency generic terms.
A division module that is a component of the domain diversity calculation apparatus, receives the question data in a general string form and classifies the question data into phrases;
The domain-specific score calculation module, which is a component of the domain diversity calculation apparatus, calculates the frequency of occurrence of a query or the appearance probability of a query in the query data and the occurrence probability of a general corpus A domain-specific score calculation step of comparing a frequency of occurrence of a word or a probability of occurrence of a word in the domain and determining a domain-specific score that is a score of how characteristic the word is in the domain through the difference;
Wherein the high frequency general term classification module which is a component of the domain diversity calculation device calculates a domain specific word based on the domain specific word calculated by the domain specific score calculation module, A high frequency general term classification step of classifying a high frequency general term having a frequency of occurrence of a word or a probability of occurrence of a word among the general terms;
A clustering step of clustering the query data into at least one similar sentence group based on a degree of similarity with the high frequency general term classified by the high frequency general term classification module, the clustering module being a component of the domain diversity calculation apparatus;
Wherein the classification module, which is a component of the domain diversity calculation apparatus, calculates a plurality of sentences included in the similar sentence group clustered on the basis of the degree of similarity with the high frequency general term in the clustering module based on the degree of similarity with the domain- Classified into at least one domain similar sentence group; And
A domain diversity calculation module that is a component of the domain diversity calculation apparatus calculates a domain diversity calculation for calculating the domain diversity of the similar sentence group based on the number of the domain similar sentence groups classified in the similar sentence group by the classification module step;
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Domain Diversity Calculation Method Using Domain - Specific Terms and High Frequency General Terms.
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