• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.9-17
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• 2019

As the artificial intelligence technology has developed recently, researches on chatbots that provide information and contents desired by users through an interactive interface have become active. Since chatbots require a variety of natural language processing technology and domain knowledge including typos and slang, it is currently limited to develop chatbots that can carry on daily conversations in a general-purpose domain. In this study, we propose an artificial intelligence chatbot that can provide real-time public traffic information and route information. The proposed chatbot has an advantage that it can understand the intention and requirements of the user through the conversation on the messenger platform without map application.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.530-540
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• 2003

As the amount of information on Internet sites increases, it becomes more necessary to provide information in efficient ways. However, information search methods based on Boolean combination of keywords that most sites provide are difficult to express user's intention adequately so that there are numerous unexpected search results. This paper proposes a conversational agent that provides users with accurate information in a friendly manner through natural language conversation. The agent recognizes user's intention by applying finite state automata to natural language queries, utilizes the intention for structured pattern matching with response knowledge, and thus provides answers that are robust against changes in word order and consistent with the user's intention. To show its practical utility, the agent is applied to the problem of introducing a Web site. The results show that the conversational agent has the ability to provide accurate and friendly responses.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.417-428
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• 1996

In this paper, a database interface system with natural language dialogue is designed and implemented. The system is made up of language analysis, context processing, dialogue processing and DB processing unit. The method for classifying and processing an undefined word in language analysis is proposed. It reduces the dictionary size, which gives difficulties in DB Interface. And the current DB Interfaces dealt with an input utterance independently. But the system in this paper provides a user with the interface environment in which he or she can have a continuous conversation with the system and retrieve DB information. Thus in this paper, speech acts which include user's inattentions well as propositional contents are defined, and user action hierarchical model for library DB retrieval is constructed. And the system uses the defined knowledge to recognize-user's plan, effectively understanding and managing the ongoing dialogue. And the system is implemented in the domain of library database in order to prove the proposed methods in this paper.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.89-98
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• 2004

Conversational agent is a system that provides users with proper information and maintains the context of dialogue on the natural language. Analyzing and modeling process of user's query is essential to make it more realistic, for which Bayesian network is a promising technique. When experts design the network for a domain, the network is usually very complicated and is hard to be understood. The separation of variables in the domain reduces the size of networks and makes it easy to design the conversational agent. Composing Bayesian network as two stages, we aim to design conversational agent easily and analyze user's query in detail. Also, previous information of dialogue makes it possible to maintain the context of conversation. Actually implementing it for a guide of web pages, we can confirm the usefulness of the proposed architecture for conversational agent.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.9-17
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• 2021

Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.61-69
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• 2009

This paper presents an improved method measuring inter-utterance similarity in an example-based dialogue system, which searches the most similar utterance in a dialogue database to generate a response to a given user utterance. Unlike general inter-sentence similarity measures, the inter-utterance similarity measure for example-based dialogue system should consider not only word distribution but also various linguistic features, such as affirmation/negation, tense, modality, sentence type, which affects the natural conversation. However, previous approaches do not sufficiently reflect these features. This paper proposes a new utterance similarity measure by analyzing and reflecting various linguistic features to improve performance in accuracy. Also, by considering substitutability of the features, the proposed method can utilize limited number of examples. Experimental results show that the proposed method achieves 10%p improvement in accuracy compared to the previous method.

• Korean Journal of Childcare and Education
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• v.7 no.1
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• pp.93-120
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• 2011

This study was aimed at inquiring into the role characteristics of a early childhood teacher, who can put an act of tending & examining into practice, and organizing a role by analyzing the concrete reality based on the relationship with a early childhood teacher's role in Korea and Japan. As a result, this study could take a look at the characteristics, such as relation with conversation, relation through sensation and body, relation with familiarity, and relation by living through the cases in Korea. As for the cases in Japan, there appear the characteristics, such as relation through natural things, relation as an equal participant, relation with bodily movement, and relation with daily life. Based on the result of the analysis, this study organized a early childhood teacher's role as relationship through object-oriented language, relationship based on sensation, relationship based on fellowship, and ecological relationship.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.1-8
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• 2002

As Internet grows, many web sites have been built, therefore much information has been registered. Because the web sites have more information, it is more difficult that the user can find the information wanted. Therefore, to get information that user wants easily, the full-text engine may be embedded to the web site. This paper is about developing the help conversational agent for a user to find the information that he wants through conversation with agent. The proposed method is based on the pattern matching of artificial intelligence, not natural language processing. If a user inputs any sentence, the help conversational agent responds to the sentence through preprocessing and pattern matching with knowledge. The knowledge is built with the XML format. With the approximate pattern matching, the agent picks up the appropriate response with some degree of similarities. At the experiment, some different sentences with the same meaning have been entered, then the agent recognized them as the same pattern, and it made a correct answer.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.135-145
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• 2023

ChatGPT is an artificial intelligence-based conversation agent developed by OpenAI using natural language processing technology. In this study, an empirical study was conducted on incumbent teachers on the intention to use the newly emerged Chat GPT. First, we studied how accuracy, entertainment, system accessibility, perceived usefulness, and perceived ease of use affect ChatGPT's acceptance intention. In addition, we analyzed whether perceived usefulness and perceived ease of use differ in the intention to accept depending on the digital technology of teachers. As a result of the study, the suitability of the structural equation model was generally good. Accuracy and entertainment were found to have a significant effect on perceived usefulness, and system accessibility was found to have a significant effect on perceived ease of use. In the analysis of teachers' digital technology control effects, it was found that perceived usefulness and perceived ease of use had a control effect between acceptance intentions. It was found that the group with high digital skills of teachers was strongly intended to accept the service regardless of perceived usefulness and ease of use. In the group with low digital skills of teachers, it is thought that ChatGPT's service shows the acceptance intention only when the perceived usefulness and ease of use are high. Therefore, in the group with low digital technology, it is necessary to seek teaching activities such as the development of instructional models using ChatGPT.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.