Extracting Exact Answers to Questions Based on Structural Links

摘要

This paper presents a novel approach to extracting phrase-level answers in a question answering system. This approach uses structural support provided by an integrated Natural Language Processing (NLP) and Information Extraction (IE) system. Both questions and the sentence-level candidate answer strings are parsed by this NLP/IE system into binary dependency structures. Phrase-level answer extraction is modelled by comparing the structural similarity involving the question-phrase and the candidate answer phrase. There are two types of structural support. The first type involves predefined, specific entity associations such as Affiliation, Position, Age for a person entity. If a question asks about one of these associations, the answer-phrase can be determined as long as the system decodes such pre-defined dependency links correctly, despite the syntactic difference used in expressions between the question and the candidate answer string.