Corpus-based Question Answering for why-Questions

This paper proposes a corpus-based approach for answering why-questions. Conventional systems use hand-crafted patterns to extract and evaluate answer candidates. However, such hand-crafted patterns are likely to have low coverage of causal expressions, and it is also difficult to assign suitable weights to the patterns by hand. In our approach, causal expressions are automatically collected from corpora tagged with semantic relations. From the collected expressions, features are created to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms a baseline that uses hand-crafted patterns with a Mean Reciprocal Rank (top-5) of 0.305, making it presumably the best-performing fully implemented why-QA system.