Paper: Why Question Answering using Sentiment Analysis and Word Classes

ACL ID D12-1034
Title Why Question Answering using Sentiment Analysis and Word Classes
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

In this paper we explore the utility of sen- timent analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is moti- vated by the observation that a why-question and its answer often follow the pattern that if something undesirable happens, the reason is also often something undesirable, and if some- thing desirable happens, the reason is also of- ten something desirable. To the best of our knowledge, this is the first work that intro- duces sentiment analysis to non-factoid ques- tion answering. We combine this simple idea with semantic word classes for ranking an- swers to why-questions and show that on a set of 850 why-questions our method gains 15.2% improvement in precision at the top-1 answer over a baseline state-of-the-art QA sy...