SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Exploiting parallel texts for word sense disambiguation: an empirical study
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Journal of the American Society for Information Science and Technology
Unsupervised monolingual and bilingual word-sense disambiguation of medical documents using UMLS
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Word Sense Disambiguation: Algorithms and Applications
Word Sense Disambiguation: Algorithms and Applications
Knowledge sources for word sense disambiguation of biomedical text
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Semi-automatic entity set refinement
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus
Journal of Biomedical Informatics
The effect of ambiguity on the automated acquisition of WSD examples
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Knowledge-based and knowledge-lean methods combined in unsupervised word sense disambiguation
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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Supervised approaches to Word Sense Disambiguation (WSD) have been shown to outperform other approaches but are hampered by reliance on labeled training examples (the data acquisition bottleneck). This paper presents a novel approach to the automatic acquisition of labeled examples for WSD which makes use of the Information Retrieval technique of relevance feedback. This semi-supervised method generates additional labeled examples based on existing annotated data. Our approach is applied to a set of ambiguous terms from biomedical journal articles and found to significantly improve the performance of a state-of-the-art WSD system.