Analysis of Semantic Classes: Toward Non-Factoid Question Answering

Analysis of Semantic Classes: Toward Non-Factoid Question Answering Yun Niu Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 The task of question answering (QA) is to find the accurate and precise answer to a natural language question in some predefined text. Most existing QA systems handle fact-based questions that usually take named entities as the answers. In this thesis, we focus on a different type of QA—non-factoid QA (NFQA) to deal with more complex information needs. The goal of the present study is to propose approaches that tackle important problems in non-factoid QA. We proposed an approach using semantic class analysis as the organizing principle to answer non-factoid questions. This approach contains four major components: • Detecting semantic classes in questions and answer sources • Identifying properties of semantic classes • Question-answer matching: exploring properties of semantic classes to find relevant pieces of information • Constructing answers by merging or synthesizing relevant information using relations between semantic classes We investigated NFQA in the context of clinical question answering, and focused on three semantic classes that correspond to roles in the commonly accepted PICO format of describing clinical scenarios. The three classes are: the problem of the patient, the intervention used to treat the problem, and the clinical outcome.