Word sense disambiguation and information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A method for word sense disambiguation of unrestricted text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Lexical query paraphrasing for document retrieval
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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We investigate the effect of paraphrase generation on document retrieval performance. Specifically, we describe experiments where three information sources are used to generate lexical paraphrases of queries posed to the Internet. These information sources are: WordNet, a Webster-based thesaurus, and a combination of Webster and WordNet. Corpus-based information and wordsimilarity information are then used to rank the paraphrases. We evaluated our mechanism using 404 queries whose answers reside in the LA Times subset of the TREC-9 corpus. Our experiments show that query paraphrasing improves retrieval performance, and that performance is influenced both by the number of paraphrases generated for a query and by their quality. Specifically, the best performance was obtained usingWordNet, which improves document recall by 14% and increases the number of questions that can be answered by 8%.