Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Journal of the American Society for Information Science and Technology
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Gene symbol disambiguation using knowledge-based profiles
Bioinformatics
Word sense disambiguation across two domains: Biomedical literature and clinical notes
Journal of Biomedical Informatics
Inter-coder agreement for computational linguistics
Computational Linguistics
Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus
Journal of Biomedical Informatics
Self-training and co-training in biomedical word sense disambiguation
BioNLP '11 Proceedings of BioNLP 2011 Workshop
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The most accurate approaches to Word Sense Disambiguation (WSD) for biomedical documents are based on supervised learning. However, these require manually labeled training examples which are expensive to create and consequently supervised WSD systems are normally limited to disambiguating a small set of ambiguous terms. An alternative approach is to create labeled training examples automatically and use them as a substitute for manually labeled ones. This paper describes a large scale WSD system based on automatically labeled examples generated using information from the UMLS Metathesaurus. The labeled examples are generated without any use of labeled training data whatsoever and is therefore completely unsupervised (unlike some previous approaches). The system is evaluated on two widely used data sets and found to outperform a state-of-the-art unsupervised approach which also uses information from the UMLS Metathesaurus.