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.
['Swapna Somasundaran', 'Theresa Wilson', 'Janyce Wiebe', 'Veselin Stoyanov']
QA with Attitude: Exploiting Opinion Type Analysis for Improving Question Answering in On-line Discussions and the News