Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Searching in Medline: Query expansion and manual indexing evaluation
Information Processing and Management: an International Journal
Evaluation of query expansion using MeSH in PubMed
Information Retrieval
Exploring criteria for successful query expansion in the genomic domain
Information Retrieval
Query and document expansion with medical subject headings terms at medical Imageclef 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Voting techniques for a multi-terminology based biomedical information retrieval
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Web Semantics: Science, Services and Agents on the World Wide Web
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EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Artificial Intelligence in Medicine
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In the context of biomedical information retrieval (IR), this paper explores the relationship between the document's global context and the query's local context in an attempt to overcome the term mismatch problem between the user query and documents in the collection. Most solutions to this problem have been focused on expanding the query by discovering its context, either global or local. In a global strategy, all documents in the collection are used to examine word occurrences and relationships in the corpus as a whole, and use this information to expand the original query. In a local strategy, the top-ranked documents retrieved for a given query are examined to determine terms for query expansion. We propose to combine the document's global context and the query's local context in an attempt to increase the term overlap between the user query and documents in the collection via document expansion (DE) and query expansion (QE). The DE technique is based on a statistical method (IR-based) to extract the most appropriate concepts (global context) from each document. The QE technique is based on a blind feedback approach using the top-ranked documents (local context) obtained in the first retrieval stage. A comparative experiment on the TREC 2004 Genomics collection demonstrates that the combination of the document's global context and the query's local context shows a significant improvement over the baseline. The MAP is significantly raised from 0.4097 to 0.4532 with a significant improvement rate of +10.62% over the baseline. The IR performance of the combined method in terms of MAP is also superior to official runs participated in TREC 2004 Genomics and is comparable to the performance of the best run (0.4075).