• Journal of Digital Convergence
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• v.17 no.11
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• pp.517-522
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• 2019

In many businesses, chatbots enhance the user experience by providing the most immediate and direct feedback to user questions. The area of use of chatbots is growing. In this study, the three types of chatbot definition, command method, function, and platform are classified according to their distinct factors. In the process, the functional delimiter element is necessary for the Chatbot UX, which is a key technical element of the functional part of pattern recognition, natural language processing, semantic web, text mining, and context-aware computing. However, the limitations at this stage were also known. Based on this, we analyzed the chatbot's UX elements for Facebook, Skype, Telegram, and Google Assistant for a better user experience. Basic UI elements such as cards, quick response, command, and application of persistent menus are needed as user experience elements.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.441-442
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• 2018

본 논문에서는 입시상담을 위한 챗봇 시스템을 텍스트 유사도 기반으로 개발하였다. 텍스트를 인지하여 답변을 제공해 주는 방식이며 실시간을 요하는 데이터들은 크롤링한 데이터를 가공을 한 후 사용자에게 대답을 해주고 사용자가 답변에 얼마나 좋은 정보인지 체크하여 그에 맞는 답변을 내어 준다. 사용자의 텍스트를 인식하는 것은 텍스트 유사도를 이용하여 정확하게 인지하고 사용자의 질문과 답변을 서버 DB에 저장을 하여 비슷한 질문이 있을 경우 저장된 답변과 평점을 이용하여 답변을 제공한다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.303-308
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• 2022

A chatbot is a responsive chatting program that can communicate with people through text or voice. 'Siri' and 'Bixby' installed in smartphones are also representative artificial intelligences that use the chatbot system. With the rapid development of chatbots, users in various fields have also begun to pay attention to the food service industry. As machine learning technology developed, it became possible to use more flexible conversations, and it soon expanded to the realm of education. Userㄴs interact through conversations with chatbots, and active interactions stimulate users' desires and at the same time have a positive effect on learning motivation. Recommendation system programs using chatbots not only recommend products according to users' preferences, but also provide various additional information. This study planned a program that combined the chatbot system and tourism service. The chatbot curator will develop into a form of inducing interest and curiosity to users through learning, and then facilitating the desire for tourism. The purpose of this study is to lay the foundation for a chatbot curator based on previous studies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.843-848
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• 2022

A chatbot is a responsive chatting program that can communicate with people through text or voice. 'Siri' and 'Bixby' installed in smartphones are also representative artificial intelligences that use the chatbot system. With the rapid development of chatbots, users in various fields have also begun to pay attention to the food service industry. As machine learning technology developed, it became possible to use more flexible conversations, and it soon expanded to the realm of education. Userㄴs interact through conversations with chatbots, and active interactions stimulate users' desires and at the same time have a positive effect on learning motivation. Recommendation system programs using chatbots not only recommend products according to users' preferences, but also provide various additional information. This study planned a program that combined the chatbot system and tourism service. The chatbot curator will develop into a form of inducing interest and curiosity to users through learning, and then facilitating the desire for tourism. The purpose of this study is to lay the foundation for a chatbot curator based on previous studies.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.51-60
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• 2023

This study focuses on the design of a GPT-based system for relatively rapid technology credit assessment of SMEs. This system addresses the limitations of traditional time-consuming evaluation methods and proposes a GPT-based model to comprehensively evaluate the technological capabilities of SMEs. This model fine-tunes the GPT model to perform fast technical credit assessment on SME-specific text data. Also, It presents a system that automates technical credit evaluation of SMEs using GPT and LLM-based chatbot technology. This system relatively shortens the time required for technology credit evaluation of small and medium-sized enterprises compared to existing methods. This model quickly assesses the reliability of the technology in terms of usability of the base model.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.15-24
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• 2023

One of the objectives and aspirations of scientists and engineers ever since the development of computers has been to interact naturally with machines. Hence features of artificial intelligence (AI) like natural language processing and natural language generation were developed. The field of AI that is thought to be expanding the fastest is interactive conversational systems. Numerous businesses have created various Virtual Personal Assistants (VPAs) using these technologies, including Apple's Siri, Amazon's Alexa, and Google Assistant, among others. Even though many chatbots have been introduced through the years to diagnose or treat psychological disorders, we are yet to have a user-friendly chatbot available. A smart generative cognitive behavioral therapy with spoken dialogue systems support was then developed using a model Persona Perception (P2) bot with Generative Pre-trained Transformer-2 (GPT-2). The model was then implemented using modern technologies in VPAs like voice recognition, Natural Language Understanding (NLU), and text-to-speech. This system is a magnificent device to help with voice-based systems because it can have therapeutic discussions with the users utilizing text and vocal interactive user experience.

• Knowledge Management Research
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• v.21 no.2
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• pp.177-196
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• 2020

Since the adoption of K-IFRS(Korean International Financial Reporting Standards), the amount of financial footnotes has been increased. However, due to the stereotypical phrase and the lack of conciseness, deriving the core information from footnotes is not really easy yet. To propose a solution for this problem, this study tried financial footnote analysis for financial ratio predictions based on text-mining techniques. Using the financial statements data from 2013 to 2018, we tried to predict the earning per share (EPS) of the following quarter. We found that measured prediction errors were significantly reduced when text-mined footnotes data were jointly used. We believe this result came from the fact that discretionary financial figures, which were hardly predicted with quantitative financial data, were more correlated with footnotes texts.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.381-390
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• 2022

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word 'depress,' using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.29-34
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• 2021

With the advent of the 4th industrial revolution era, IT technology is creating new services that have not existed by converging with various existing industries and fields. In particular, in the field of artificial intelligence, chatbots and the latest technologies have developed dramatically with the development of natural language processing technology, and various business processes are processed through chatbots. This study is a study on a system that provides a close answer to the question the user wants to find by creating a structural form for legal inquiries through Slot Filling-based chatbot technology, and inputting a predetermined type of question. Using the proposal system, it is possible to construct question-and-answer data in a more structured form of legal information, which is unstructured data in text form. In addition, by managing the accumulated Q&A data through a big data storage system such as Apache Hive and recycling the data for learning, the reliability of the response can be expected to continuously improve.