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(A Question Type Classifier based on a Support Vector Machine for a Korean Question-Answering System)  

김학수 (서강대학교 컴퓨터학과 자연어처리 연구실)
안영훈 (서강대학교 컴퓨터학과 자연어처리 연구실)
서정연 (서강대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.5_6, 2003 , pp. 466-475 More about this Journal
Abstract
To build an efficient Question-Answering (QA) system, a question type classifier is needed. It can classify user's queries into predefined categories regardless of the surface form of a question. In this paper, we propose a question type classifier using a Support Vector Machine (SVM). The question type classifier first extracts features like lexical forms, part of speech and semantic markers from a user's question. The system uses $X^2$ statistic to select important features. Selected features are represented as a vector. Finally, a SVM categorizes questions into predefined categories according to the extracted features. In the experiment, the proposed system accomplished 86.4% accuracy The system precisely classifies question type without using any rules like lexico-syntactic patterns. Therefore, the system is robust and easily portable to other domains.
Keywords
Question type classifier; Document categorization; Support Vector Machine;
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Times Cited By KSCI : 1  (Citation Analysis)
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