• International Journal of Contents
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• v.1 no.2
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• pp.39-43
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• 2005

In this paper, we describe a generation mechanism of semantic-based queries for high accuracy information retrieval and question answering. It is difficult to offer the correct retrieval result because general information retrieval systems do not analyze the semantic of user's natural language question. We analyze user's question semantically and extract semantic features, and we .generate semantic-based queries using them. These queries are generated using the se-mantic-based question analysis grammar and the query generation rule. They are represented as semantic features and grammatical morphemes that consider semantic and syntactic structure of user's questions. We evaluated our mechanism using 100 questions whose answer type is a person in the TREC-9 corpus and Web. There was a 0.28 improvement in the precision at 10 documents when semantic-based queries were used for information retrieval.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.271-286
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• 2006

In this paper, we introduce automatic question generation technique using the language resources like User-Word Intelligent Network(U-WIN) and Korean dictionary including quite a for of information. And we present Korean word learning system with this technique. The item pool method which almost learning-system are using makes some problems. As a solution of the problems, we classified into 8 question type and implemented the Korean word learning system which is making the Korean question automatically by using the morphological and semantic information according to the automatic question generation pattern of each type.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.27-36
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• 2019

In this paper, we propose a method to find the most similar answer to the user's response from the question-answer database in order to avoid generating a redundant question in retrieval-based automatic question generation system. As a question of the most similar answer to user's response may already be known to the user, the question should be removed from a set of question candidates. A similarity detector calculates a similarity between two answers by utilizing the same words, paraphrases, and sentential meanings. Paraphrases can be acquired by building a phrase table used in a statistical machine translation. A sentential meaning's similarity of two answers is calculated by an attention-based convolutional neural network. We evaluate the accuracy of the similarity detector on an evaluation set with 100 answers, and can get the 71% Mean Reciprocal Rank (MRR) score.

• Journal of Korean Elementary Science Education
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• v.43 no.4
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• pp.493-503
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• 2024

This study investigated the changes in elementary school students' question-generation abilities, science achievements, and attitudes toward science after attending an argument generation class. The study was conducted with 5th grade students of H Elementary School in G-si, Gyeonggi-do, and the following results were noted. First, after attending the argument generation class, the students' question-generation ability significantly improved. Second, there was no significant difference in the students' science achievement. However, according to the teacher's reflection journal, conceptual changes could be seen in the students' thinking as a result of participating in the argument-generation activities, which was confirmed by the students' reports. Third, there was no statistically significant difference in the students' attitudes toward science. However, there was a change in their attitude toward participating in the science classes, and there was a positive change in the number of the students participating in the science classes as a result of the activities.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.439-446
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• 2024

This study investigates the potential of Retrieval-Augmented Generation (RAG)-based Question Answering (QA) technology for accurate and relevant responses of Large Language Models (LLMs) to construction safety-related queries. Despite LLMs' advancements, their application, especially a Q&A Chatbot faces challenges due to hallucination and lack of domain-specific details. This study explores RAG's potentials to mitigate these issues by making LLM refer to external databases, such as the OSHA Field Safety and Health Manual, for generating precise and factual contents. A comparative analysis of different RAG technologies-Naïve-RAG, Rerank-RAG, and Iterative Retrieval-Generation-demonstrates their effectiveness over traditional LLM approaches. The findings highlight RAG's significance in producing structured, fact-based responses, underscoring its superiority in addressing the domain-specific informational needs regarding construction safety practices. This research marks a step forward in the application of generative AI technologies to enhance safety standards and practices within the construction industry.

• Journal of Korean Medical classics
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• v.22 no.4
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• pp.189-198
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• 2009

Considering the opinions of annotators, the remedy about excess of east and deficiency of west from "the seventy-fifth question" can be arranged as follows. "The seventy-fifth question", with "the sixty-ninth question", explains excess and deficiency of mother and son. Abatement of fire and invigoration of water[瀉火補水] in the excess of wood and deficiency of metal[木實金虛] presents a remedy, which has been applied in herbs and medicine application henceforth. "The seventy-fifth question" is a unique theory from " Nan-gyeong(難經)", and does not continue the theory of "Hwangjenaegyeong(黃帝內經)". "The seventy-fifth question" mentions the relationship of excess and deficiency of the five elements and five viscera, but does not mention excess and deficiency of invigoration and abatement of the meridian. Remedy from abatement of fire and invigoration of water[瀉火補水] in the excess of wood and deficiency of metal[木實金虛] is an abnormal, temporary and extraordinary method. This remedy is applied in Saam acupuncture[舍巖鍼] as A-variation form. The process where Son allows excess of mother[子能令母實] and mother allows deficiency of son[母能令子虛] in the abatement of fire and invigoration of water[瀉火補水] is a mechanism, not a remedy. Generation after generation, medical practitioners can be classified into those that claimed abatement of fire and invigoration of water[瀉火補水] because of the relation with excess of liver and deficiency of lung[肝實肺虛], abatement of heart(瀉心) due to the excess of liver(肝實), or invigoration of Eum and abatement of Yang[補陰瀉陽].

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2718-2731
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• 2023

This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG). Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence. The main goal of our method is to generate questions from a single sentence. These generated questions are known as factoid questions which require short, fact-based answers. In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language. It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules. To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook. Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task. These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition. We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions. The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.285-291
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• 2001

This paper describes a natural language question answering system that can be used by students in getting as solution to their queries. Unlike AI question answering system that focus on the generation of new answers, the present system retrieves existing ones from question-answer files. Unlike information retrieval approaches that rely on a purely lexical metric of similarity between query and document, it uses a semantic knowledge base (WordNet) to improve its ability to match question. Paper describes the design and the current implementation of the system as an intelligent tutoring system. Main drawback of the existing tutoring systems is that the computer poses a question to the students and guides them in reaching the solution to the problem. In the present approach, a student asks any question related to the topic and gets a suitable reply. Based on his query, he can either get a direct answer to his question or a set of questions (to a maximum of 3 or 4) which bear the greatest resemblance to the user input. We further analyze-application fields for such kind of a system and discuss the scope for future research in this area.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.49-59
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• 2002

There have been many attempts to educationally utilize a computer. As a result, a number of learning programs and testing programs have been developed. Testing programs developed so far are rather focusing on giving learners questions without interactivity. Question givers also have to feed question data into a computer. Therefore, this thesis, firstly, explains how learners themselves feed question data into a computer. Secondly, this thesis explains how to develop such a system that can produce new questions based on the question data fed by learners. This paper also introduces the system environment, which features the test item generation system, and explains the system environment by exemplifying internal modules, a test item bank. Besides, we also describe how to apply this system to educational programs and identify whether it is possible to apply it to a Chinese subject and others.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.144-147
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• 2015

Question Answering (QA) services can provide exact answers to user questions written in natural language form. This research focuses on how to build a QA system for a specific domain area. Online and offline QA system architecture of targeted domain such as domain detection, question analysis, reasoning, information retrieval, filtering, answer extraction, re-ranking, and answer generation, as well as data preparation are presented herein. Test results with an official Frequently Asked Question (FAQ) set showed 68% accuracy of the top 1 and 77% accuracy of the top 5. The contribution of each part such as question analysis system, document search engine, knowledge graph engine and re-ranking module for achieving the final answer are also presented.