Browse > Article
http://dx.doi.org/10.3745/KTSDE.2015.4.7.283

Recognition of Answer Type for WiseQA  

Heo, Jeong (울산대학교 정보통신공학, 한국전자통신연구원)
Ryu, Pum Mo (한국전자통신연구원)
Kim, Hyun Ki (한국전자통신연구원)
Ock, Cheol Young (울산대학교 전기공학부 IT융합전공)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.7, 2015 , pp. 283-290 More about this Journal
Abstract
In this paper, we propose a hybrid method for the recognition of answer types in the WiseQA system. The answer types are classified into two categories: the lexical answer type (LAT) and the semantic answer type (SAT). This paper proposes two models for the LAT detection. One is a rule-based model using question focuses. The other is a machine learning model based on sequence labeling. We also propose two models for the SAT classification. They are a machine learning model based on multiclass classification and a filtering-rule model based on the lexical answer type. The performance of the LAT detection and the SAT classification shows F1-score of 82.47% and precision of 77.13%, respectively. Compared with IBM Watson for the performance of the LAT, the precision is 1.0% lower and the recall is 7.4% higher.
Keywords
Question Answering; Answer Type; Question Analysis; WiseQA;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 John Burger, Claire Cardie, Vinay Chaudhri, Robert Gaizauskas, Sanda Harabagiu, David Israel, Christian Jacquemin, Chin-Yew Lin, Steve Maiorano, George Miller, Dan Moldovan, Bill Ogden, John Prager, Ellen Riloff, Amit Singhal, Rohini Shrihari, Tomek Strzalkowski, Ellen Voorhees, and Ralph Weishedel, "Issues, Tasks and Program Structures to Roadmap Research in Question & Answering (Q&A)," Document Understanding Conferences Roadmapping Documents, 2001.
2 FERRUCCI, David A., "Introduction to "this is watson"," IBM Journal of Research and Development, Vol.56, No.3.4, pp. 1:1-1:15, 2012.
3 Prager, J., Chu-Carroll, J., Czuba, K., Welty, C., Ittycheriah, A., & Mahindru, R., "IBM's PIQUANT in TREC2003," pp.283-292, TREC, 2003.
4 Chu-Carroll, J., Czuba, K., Prager, J. M., Ittycheriah, A., and S, "IBM's PIQUANT II in TREC 2004," in TREC, 2004.
5 Dan Moldovan, Sanda Harabagiu, Marius Pasca, Rada Mihalcea, Roxana Girju, Richard Goodrum, and Vasile Rus, "The Structure and Performance of an Open-Domain Question Answering System," in Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, 2000.
6 Pasca, Marius A., and Sandra M. Harabagiu, "High performance question/answering," Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2001.
7 LALLY, Adam, et al. "Question analysis: How Watson reads a clue," IBM Journal of Research and Development, Vol.56, No.3.4, pp.2:1-2:14, 2012.
8 Choe Ho-seop, "Construction Method of Large-scale 'Urimal(Korean)-Word Intelligent Network'," Hangul 273, pp.125-141, 2006(in Korean).
9 Aesun Yoon, Soonhee Hwang, Eunryoung Lee, and Hyukchul Kwon, "Construction of Korean Wordnet KorLex 1.5," Journal of KIISE: Software and Applications, Vol.36, No.1, pp.92-108, Korea, 2009.
10 C. Lee and M. Jang, "Named Entity Recognition with Structural SVMs and Pegasos algorithm," Korean Journal of Cognitive Science, Vol.21, No.4, pp.655-667, Korea, 2010.   DOI
11 Jeong Heo, Pum-Mo Ryu, Myung-Gil Jang, and Hyun-Ki Kim, "Search Space Reduction and Answer Type Classification for Open Domain Q&A," Journal of KIISE: Software and Applications, Vol.39, No.2, pp.118-132, Korea, 2012.
12 KALYANPUR, Aditya, et al. "Fact-based question decomposition in DeepQA," IBM Journal of Research and Development, Vol.56, No.3.4, pp.13:1-13:11, 2012.