Long-answer question answering and rhetorical-semantic relations

DefScriber is presented, a system for answering definitional, biographical and other long-answer questions using a hybrid of goaland data-driven methods, motivated by a set of definitional predicates which capture information types commonly useful in definitions.

Abstract

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 goal- and data-driven methods. Our goal-driven, or top-down, approach is motivated by a set of definitional pred-