The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
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
Journal of the American Society for Information Science and Technology
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Acquiring sense tagged examples using relevance feedback
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus
Journal of Biomedical Informatics
Sense-based biomedical indexing and retrieval
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
An approach based on langage modeling for improving biomedical information retrieval
International Journal of Knowledge-based and Intelligent Engineering Systems
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Like text in other domains, biomedical documents contain a range of terms with more than one possible meaning. These ambiguities form a significant obstacle to the automatic processing of biomedical texts. Previous approaches to resolving this problem have made use of a variety of knowledge sources including linguistic information (from the context in which the ambiguous term is used) and domain-specific resources (such as UMLS). In this paper we compare a range of knowledge sources which have been previously used and introduce a novel one: MeSH terms. The best performance is obtained using linguistic features in combination with MeSH terms. Results from our system outperform published results for previously reported systems on a standard test set (the NLM-WSD corpus).