WordNet: a lexical database for English
Communications of the ACM
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
IR-n: A Passage Retrieval System at CLEF-2001
CLEF '01 Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross-Language Information Retrieval Systems
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Analyses for elucidating current question answering technology
Natural Language Engineering
The effect of document retrieval quality on factoid question answering performance
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic search via XML fragments: a high-precision approach to IR
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
QuestionBank: creating a corpus of parse-annotated questions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Lydia: a system for large-scale news analysis
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
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Question answering (QA) is the task of finding a concise answer to a natural language question. The first stage of QA involves information retrieval. Therefore, performance of an information retrieval subsystem serves as an upper bound for the performance of a QA system. In this work we use phrases automatically identified from questions as exact match constituents to search queries. Our results show an improvement over baseline on several document and sentence retrieval measures on the WEB dataset. We get a 20% relative improvement in MRR for sentence extraction on the WEB dataset when using automatically generated phrases and a further 9.5% relative improvement when using manually annotated phrases. Surprisingly, a separate experiment on the indexed AQUAINT dataset showed no effect on IR performance of using exact phrases.