MURAX: a robust linguistic approach for question answering using an on-line encyclopedia
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A question answering system supported by information extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Question Answering on a case insensitive corpus
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Sentence Topics Based Knowledge Acquisition for Question Answering
IEICE - Transactions on Information and Systems
A LF based answer indexing method for encyclopedia question-answering system
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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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 used in expressions between the question and the candidate answer string. The second type involves generic grammatical relationships such as V-S (verb-subject), V-O (verb-object).Preliminary experimental results show an improvement in both precision and recall in extracting phrase-level answers, compared with a baseline system which only uses Named Entity constraints. The proposed methods are particularly effective in cases where the question-phrase does not correspond to a known named entity type and in cases where there are multiple candidate answer-phrases satisfying the named entity constraints.