Recognition of Answer Type for WiseQA


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 7, pp. 283-290, Jul. 2015
10.3745/KTSDE.2015.4.7.283, Full Text:

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.


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Cite this article
[IEEE Style]
J. Heo, P. M. Ryu, H. K. Kim and C. Y. Ock, "Recognition of Answer Type for WiseQA," KIPS Transactions on Software and Data Engineering, vol. 4, no. 7, pp. 283-290, 2015. DOI: 10.3745/KTSDE.2015.4.7.283.

[ACM Style]
Jeong Heo, Pum Mo Ryu, Hyun Ki Kim, and Cheol Young Ock. 2015. Recognition of Answer Type for WiseQA. KIPS Transactions on Software and Data Engineering, 4, 7, (2015), 283-290. DOI: 10.3745/KTSDE.2015.4.7.283.