QA with Attitude: Exploiting Opinion Type Analysis for Improving Question Answering in On-line Discussions and the News
Published 1 January 2007
Swapna Somasundaran, Theresa Wilson, Janyce Wiebe, Veselin Stoyanov
Citations66
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TL;DR
This paper presents a set of attitude types developed with an eye toward QA and shows that they can be reliably annotated, and develops automatic classifiers for recognizing two main types of attitudes: sentiment and arguing.
Abstract
In this paper, we explore the utility of attitude types for improving question answering (QA) on both web-based discussions and news data. We present a set of attitude types developed with an eye toward QA and show that they can be reliably annotated. Using the attitude annotations, we develop automatic classifiers for recognizing two main types of attitudes: sentiment and arguing. Finally, we exploit information about the attitude types of questions and answers for improving opinion QA with promising results. 1.
Keywords
Computer Science
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