• International Journal of Contents
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• v.2 no.1
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• pp.17-21
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• 2006

In this paper, we describe a concept-based question-answering system in which concept rather than keyword itself makes an important role on both question analysis and answer extraction. Our idea is that concepts occurred in same type of questions are similar, and if a question is analyzed according to those concepts then we can extract more accurate answer because we know the semantic role of each word or phrase in question. Concept frame is defined for each type of question, and it is composed of important concepts in that question type. Currently the number of question type is 79 including 34 types for person, 14 types for location, and so on. We experiment this concept-based approach about questions which require person s name as their answer. Experimental results show that our system has high accuracy in answer extraction. Also, this concept-based approach can be used in combination with conventional approaches.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.927-933
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• 2018

One of the factors that have the greatest influence on the error of the question answering system that finds and provides answers to natural language questions is the step of searching for documents or passages that contain correct answers. In order to improve the retrieval performance, it is necessary to understand the characteristics of documents and passages containing correct answers. This paper experimentally analyzes how many question words appear in the correct answer documents, how the location of the question word is distributed, and how the topic of the question and the correct answer document are similar using the corpus composed of the question, the documents with correct answer, and the documents without correct answer. This study explains the causes of previous search research results for question answer system and discusses the necessary elements of effective search step.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.209-215
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• 2005

In this paper, we describe the answer pattern for definitional question-answering system. The .answer extraction method of a definitional question-answering system is different from the general answer extraction method because it presents the descriptive answer for a definitional question. The definitional answer extraction using the definitional answer pattern can extract the definitional answer correctly without the semantic analysis. The definitional answer pattern is consist of answer pattern, conditional rule and priority to extract the correct definitional answer. We extract the answer pattern from the definitional training corpus and determine the optimum conditional rule using F-measure. Next, we determine the priority of answer patterns using precision and syntactic structure. Our experiments show that our approach results in the precision(0.8207), the recall(0.9268) and the F-measure(0.8705). It means that our approach can be used efficiently for a definitional question-answering system.

• Journal of Sasang Constitutional Medicine
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• v.18 no.1
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• pp.91-106
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• 2006

1. Objectives The purpose of this study is to find out the characteristics about the Sasang constitution of elementary school students based on the questions that have significant differences. 2. Methods 146 children who have visited Kang-Nam Kyung-Hee Oriental Hospital Sasang constitution center from Mar. 2003 to May. 2005, were investigated through the questionnaires. These have the categories of personality and emotional characteristics, personal relationship, playing and the way to handle things such as jobs or missions, behavioral characteristics, and etc., were analyzed statistically. 3. Results and Conclusions (1) In the category of the 'Personality and Emotional characteristics', significantly more Soeumin showed positive answers to the question, 'being easily nervous and get irritated' than the other groups, and significantly more Taeumin to the question, 'considerate and thoughtful' than Soyangin. (2) In the category of the 'Personal relationship', there were significantly more Soyangin who showed positive answer to the question 'making a friend easily' than the other groups. (3) In the category of the 'playing and the way to handle things', significantly more Soeumin showed positive answer to the question, 'love to do the exercise' than Soyangin, and significantly more Soeumin showed positive answer to the question, 'not careful and meticulous' than Soyangin, and significantly more Soeumin showed positive answer to the question, 'like to read books' than Taeumin. (4) In the category of the 'Behavioral characteristics', significantly more Soyangin showed positive answer to the question, 'unable to concentrate just one thing even for just a minute and keep on moving.' than the other groups, and significantly more Soyangin showed positive answer to the question, 'act or response quick.' than Taeumin, and significantly more Soyangin showed positive answer to the question, 'behave and go around restlessly and flighty' than Soeumin, and significantly more Taeumin showed the positive answer to the question, 'hate to move by oneself' than Soyangin.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.448-451
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• 2005

In this paper, we describe a method of answer extraction on a concept-based question-answering system. The concept-based question answering system is a system which extract answer using concept information. we have researched the method of answer extraction using concepts which analyzed and extracted through question analysing with answer extracting rules. We analyzed documents including answers and then composed answer extracting rules. Rules consist of concept and syntactic information, we generated candidates of answer through the rules and then chose answer.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.83-98
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• 2004

The purpose of this study is to review the characteristics of a web-based question-answer service and to analyze the information needs and use behavior of the service for a more efficient question-answer service plan. On the basis of the findings, this study makes suggestions for the question-answer service in respect of reinforcing the effectiveness of information itself and the efficiency of question-answer services. The speciality. accuracy. and specificity including the variety of themes should be improved for more effective information. Also. interactivity. readiness. and convenience should be improved for a more efficient service.

• ETRI Journal
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• v.39 no.2
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• pp.191-201
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• 2017

This paper proposes an approach using taxonomic relatedness for answer-type recognition and type coercion in a question-answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule-based approach of IBM's Watson, our LAT detector, which combines rule-based and machine-learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness-based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer-type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.10-20
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• 2007

This paper describes a concept-based question analysis to analyze concept which is more important than keyword for the accurate answer extraction. Our idea is that we can extract correct answers from various paragraphs with different structures when we use well-defined concepts because concepts occurred in questions of same answer type are similar. That is, we will analyze the syntactic and semantic role of each word or phrase in a question in order to extract more relevant documents and more accurate answer in them. For each answer type, we define a concept frame which is composed of concepts commonly occurred in that type of questions and analyze user's question by filling a concept frame with a word or phrase. Empirical results show that our concept-based question analysis can extract more accurate answer than any other conventional approach. Also, concept-based approach has additional merits that it is language universal model, and can be combined with arbitrary conventional approaches.

• 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 the Korea Academia-Industrial cooperation Society
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• v.5 no.6
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• pp.539-545
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• 2004

For question answering system that extracts an answer and output to user‘s natural language question, a process of question type classification from user’s natural language query is very important. This paper proposes a question and answer type classifier using the interrogatives and word semantic categories instead of complicated classifying rules and huge dictionaries. Synonyms and postfix information are also used for question type classification. Experiments show that the semantic categories are helpful for question type classifying without interrogatives.