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Similar Question Search System for online Q&A for the Korean Language Based on Topic Classification  

Mun, Jung-Min (Kumoh National Institute of Technology)
Song, Yeong-Ho (Kumoh National Institute of Technology)
Jin, Ji-Hwan (Kumoh National Institute of Technology)
Lee, Hyun-Seob (Kumoh National Institute of Technology)
Lee, Hyun Ah (Kumoh National Institute of Technology)
Publication Information
Korean Journal of Cognitive Science / v.26, no.3, 2015 , pp. 263-278 More about this Journal
Abstract
Online Q&A for the National Institute of the Korean Language provides expert's answers for questions about the Korean language, in which many similar questions are repeatedly posted like other Q&A boards. So, if a system automatically finds questions that are similar to a user's question, it can immediately provide users with recommendable answers to their question and prevent experts from wasting time to answer to similar questions repeatedly. In this paper, we set 5 classes of questions based on its topic which are frequently asked, and propose to classify questions to those classes. Our system searches similar questions by combining topic similarity, vector similarity and sequence similarity. Experiment shows that our method improves search correctness with topic classification. In experiment, Mean Reciprocal Rank(MRR) of our system is 0.756, and precision for the first result is 68.31% and precision for top five results is 87.32%.
Keywords
Question Answering System; Similar Question Search; Topic Classification; online Q&A of the National Institute of the Korean Language;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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