• 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.

• 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.

• 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 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.6 no.3
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• pp.12-19
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• 2006

In this paper, we describe a deep analysis of question for question answering system. It is difficult to offer the correct answer because general question answering systems do not analyze the semantic of user's natural language question. We analyze user's question semantically and extract semantic features using the semantic feature extraction grammar and characteristics of natural language question. They are represented as semantic features and grammatical morphemes that consider semantic and syntactic structure of user's questions. We evaluated our approach using 100 questions whose answer type is a person in the web. We showed that a deep analysis of questions which are comparatively short but enough to mean can analysis the user's intention and extract semantic features.

• 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.

• 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.

• 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.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.405-416
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• 2010

Knowledge search system is to find the question-answer documents for user question. Even highly qualified question-answer documents could be far different from those that a user want to find. The reason for this failure is that user frequently fails to make user's question to express his/her intension precisely. In this paper, we show our newly developed knowledge search system that recommends additional question-answer documents to include the contents that user want to find with high probability.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.133-136
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• 2003

Due to recent explosion of information available online, question- answering (Q&A) systems are becoming a compelling framework for finding relevant information in a variety of domains. Question-answering system is one of the best ways to introduce a novice customer to a new domain without making him/her to obtain prior knowledge of its overall structure improving search request with specific answer. However, the current web poses serious problem for finding specific answer for many overlapped meanings for the same questions or duplicate questions also retrieved answer for many overlapped meanings fer the same questions or duplicate questions also retrieved answer is slow due to enhanced network traffic, which leads to wastage of resource. In order to avoid wrong answer which occur due to above-mentioned problem we propose the system using ontology by RDF, RDFS and mobile agent based on JAVA. We also choose wireless internet based embedded device as our test bed for the system and apply the system in E-commerce information domain. The mobile agent provides agent routing with reduced network traffic, consequently helps us to minimize the elapsed time for answers and structured ontology based on our proposed algorithms sorts out the similarity between current and past question by comparing properties of classes.