• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2406-2412
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• 2007

Recently, there are many researches about natural language processing programs using artificial intelligence methods. But the researches mostly concentrate on Korean morphological analyses and there are few researches about application of the results of Korean morphological analyses. In this paper, we implemented a natural language conversation program that NPC in games can talk with users by natural language sentences using the results of morphological analyses and a rule-based inference method. We proposed representation and implementation methods of rules suitable for the processing of natural language conversation using NEO, a rule-based inference engine. In the experiment using rules and facts about knowledge of conversation for diet counselor NPC, we could verify that natural conversation results were produced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• Korean Journal of Cognitive Science
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• v.3 no.2
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• pp.291-309
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• 1992

The computer manipulation by means of natural language prcessing will be not only helpful to use the computer with more simple and more comfortable but also flexiable to communicate between human and computers.In this paper the natural language interfaces will be applied to an intelligent geography tutoring systems(IGTS),and we will inspect the something to consider in the case of its implementation.Each module of IGTS is connected to the interface module and correspondence with each other for the sake of natural conversation between system and learner.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.26-47
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• 2022

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.129-136
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• 2010

In this paper, we present an Example-based Dialogue System for English conversation tutoring. It aims to provide intelligent one-to-one English conversation tutoring instead of old fashioned language education with static multimedia materials. This system can understand poor expressions of students and it enables green hands to engage in a dialogue in spite of their poor linguistic ability, which gives students interesting motivation to learn a foreign language. And this system also has educational functionalities to improve the linguistic ability. To achieve these goals, we have developed a statistical natural language understanding module for understanding poor expressions and an example-based dialogue manager with high domain scalability and several effective tutoring methods.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.13-21
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• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.429-435
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• 2021

In this study, the creation of emotional dialogue was investigated within the process of developing a robot's natural language understanding and emotional dialogue processing. Unlike an English-based dataset, which is the mainstay of natural language processing, the Korean-based dataset has several shortcomings. Therefore, in a situation where the Korean language base is insufficient, the Korean dataset should be dealt with in detail, and in particular, the unique characteristics of the language should be considered. Hence, the first step is to base this study on a specific Korean dataset consisting of conversations on emotional topics. Subsequently, a model was built that learns to extract the continuous dialogue features from a pre-trained language model to generate sentences while maintaining the context of the dialogue. To validate the model, a chatbot system was implemented and meaningful results were obtained by collecting the external subjects and conducting experiments. As a result, the proposed model was influenced by the dataset in which the conversation topic was consultation, to facilitate free and emotional communication with users as if they were consulting with a chatbot. The results were analyzed to identify and explain the advantages and disadvantages of the current model. Finally, as a necessary element to reach the aforementioned ultimate research goal, a discussion is presented on the areas for future studies.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.183-190
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• 1996

The purpose of this paper is to show that the solutions of the problem for the anaphor ocured in korean senstence, by means of one-direction activated chart parsing leaded by a head. This is the phenomenon frequently occured in the conversation of natural language and the part necessarily required in the construction of natural language processing system for the practical use. To solve the problem of anaphor in the korean language, we have computerized definition and the management conditions necessary in the semantic classification between the anaphor and its antecedent and index are added in the feature structure in lexicon. To deal with anaphor in parser and algorithm is proposed to solve the problem for anaphor. The range of management of pareser is extended to solve the problem for anaphor of the indeclinable parts of speech in korean occured in all the sentences the parser HPSG developed previously manages.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.87-96
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• 2023

ChatGPT, a leader in generative AI, can use natural expressions like humans based on large-scale language models (LLM). The ability to grasp the context of the language and provide more specific answers by algorithms is excellent. It also has high-quality conversation capabilities that have significantly developed from past Chatbot services to the level of human conversation. In addition, it is expected to change the operation method of the tourism industry and improve the service by utilizing ChatGPT, a generative AI in the tourism sector. This study was conducted to explore ChatGPT trends and paradigms in tourism. The results of the study are as follows. First, keywords such as tourism, utilization, creation, technology, service, travel, holding, education, development, news, digital, future, and chatbot were widespread. Second, unlike other keywords, service, education, and Mokpo City data confirmed the results of a high degree of centrality. Third, due to CONCOR analysis, eight keyword clusters highly relevant to ChatGPT in the tourism sector emerged.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.17-24
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• 2018

With improvement of speech recognition and natural language processing, dialog systems are recently adapted to various service domains. It became possible to get desirable services by conversation through the dialog system, but it is still necessary to improve separate modules, such as domain detection, intention detection, named entity recognition, and out-of-domain detection, in order to achieve stable service offer. When it misclassifies an in-domain sentence of conversation as out-of-domain, it will result in poor customer satisfaction and finally lost business. As there have been relatively small number of studies related to the out-of-domain detection, in this paper, we introduce a new method using a hierarchical Dirichlet process and demonstrate the effectiveness of it by experimental results on Korean dataset.