ANALYSIS OF SEMANTIC CLASSES: TOWARD NON-FACTOID QUESTION ANSWERING
Yun Niu · TSpace (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: (1) Detecting semantic classes in questions and answer sources; (2) Identifying properties of semantic classes; (3) Question-answer matching: exploring properties of semantic classes to find relevant pieces of information; (4) 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. We built explicit connection between text summarization and identifying answer components in NFQA and constructed a summarization system that explores a supervised classification model to extract important sentences for answer construction. We investigated the role of clinical outcome and their polarity in this task. We identified an important property of semantic classes---their cores. We showed how cores of interventions, problems, and outcomes in a sentence can be extracted automatically by developing an approach exploring semi-supervised learning techniques. Another property that We analyzed is polarity, an inherent property of clinical outcomes. We developed a method using a supervised learning model to automatically detect polarity of clinical outcomes. We used rule-based approaches to identify clinical outcomes and relations between instances of interventions in sentences.