Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Information Processing and Management: an International Journal - Special issue on history of information science
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IEEE Internet Computing
An evaluation of term dependence models in information retrieval
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
Using a semantic network for information extraction
Natural Language Engineering
Semantic cores for representing documents in IR
Proceedings of the 2005 ACM symposium on Applied computing
Context-sensitive semantic smoothing for the language modeling approach to genomic IR
SIGIR '06 Proceedings of the 29th 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
Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature
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
The AMTEx approach in the medical document indexing and retrieval application
Data & Knowledge Engineering
Multiple Terminologies in a Health Portal: Automatic Indexing and Information Retrieval
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
A recent advance in the automatic indexing of the biomedical literature
Journal of Biomedical Informatics
MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Modern 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
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NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Biomedical concept extraction based on combining the content-based and word order similarities
Proceedings of the 2011 ACM Symposium on Applied Computing
Combining global and local semantic contexts for improving biomedical information retrieval
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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In the context of document retrieval in the biomedical domain, this paper introduces a novel approach to searching for biomedical information using contextual semantic information. More specifically, we propose to combine the contextual semantic information in documents and user queries in an attempt to improve the performance of biomedical information retrieval (IR) systems. Contextual information provides knowledge about a domain in a global context or statistical properties of a sub collection of documents related to a given query in a local context. In our context sensitive IR approach, terms denoting concepts are extracted from each document using several biomedical terminologies. Preferred terms denoting concepts are used to enrich the semantics of the document content via document expansion. The user query is expanded using terms extracted from the top-ranked expanded documents via a blind feedback query expansion approach. In addition, we aim to evaluate the utility of incorporating several terminologies within the proposed context sensitive approach. The experiments carried out on the TREC Genomics 2004 and 2005 test sets show that our context-sensitive IR approach significantly outperforms state-of-the-art baseline approaches.