NTT's Question Answering System for NTCIR-6 QAC-4
Ryuichiro Higashinaka, Hideki Isozaki · 2007
NTCIR-6 QAC-4 organizers announced that there would be no restriction (such as factoid) on QAC4 questions, but they plan to include many ‘definition’ questions and ‘why ’ questions. Therefore, we focused on these two question types. For ‘definition ’ questions, we used a simple pattern-based approach. For ‘why’ questions, hand-crafted rules were used in previous work for answer candidate extraction [5]. However, such rules greatly depend on developers ’ intuition and are costly to make. We adopt a supervised machine learning approach. We collected causal expressions from the EDR corpus and trained a causal expression classifier, integrating lexical, syntactic, and semantic features. The experimental results show that our system is effective for ‘why ’ and ‘definition ’ questions.