Long-answer question answering and rhetorical-semantic relations

Long-Answer Question Answering and Rhetorical-Semantic Relations Sasha J. Blair-Goldensohn Over the past decade, Question Answering (QA) has generated considerable interest and participation in the fields of Natural Language Processing and Information Retrieval. Conferences such as TREC, CLEF and DUC have examined various aspects of the QA task in the academic community. In the commercial world, major search engines from Google, Microsoft and Yahoo have integrated basic QA capabilities into their core web search. These efforts have focused largely on so-called “factoid” questions seeking a single fact, such as the birthdate of an individual or the capital city of a country. Yet in the past few years, there has been growing recognition of a broad class of “long-answer” questions which cannot be satisfactorily answered in this framework, such as those seeking a definition, explanation, or other descriptive information in response. In this thesis, we consider the problem of answering such questions, with particular focus on the contribution to be made by integrating rhetorical and semantic models. We present DefScriber, a system for answering definitional (“What is X?”), biographical (“Who is X?”) and other long-answer questions using a hybrid of goaland data-driven methods. Our goal-driven, or top-down, approach is motivated by a set of definitional predicates which capture information types commonly useful in definitions; our data-driven, or bottom-up, approach uses dynamic analysis of input data to guide answer content. In several evaluations, we demonstrate that DefScriber outperforms competitive summarization techniques, and ranks among the top long-answer QA systems being developed by others. Motivated by our experience with definitional predicates in DefScriber, we pursue a set of experiments which automatically acquire broad-coverage lexical models of rhetoricalsemantic relations (RSRs) such as Cause and Contrast. Building on the framework of Marcu and Echihabi (Marcu and Echihabi, 2002), we implement techniques to improve the quality of these models using syntactic filtering and topic segmentation, and present evaluation results showing that these methods can improve the accuracy of relation classification. Lastly, we implement two approaches for applying the knowledge in our RSR models to enhance the performance and scope of DefScriber. First, we integrate RSR models into the answer-building process in DefScriber, finding incremental improvements with respect to the content and ordering of responses. Second, we use our RSR models to help identify relevant answer material for an exploratory class of “relation-focused” questions which seek explanatory or comparative responses. We demonstrate that in the case of explanation questions, using RSRs can lead to significantly more relevant responses.