The Journal of Machine Learning Research
The role of domain information in Word Sense Disambiguation
Natural Language Engineering
Journal of the American Society for Information Science and Technology
A large scale, corpus-based approach for automatically disambiguating biomedical abbreviations
ACM Transactions on Information Systems (TOIS)
Domain-specific sense distributions and predominant sense acquisition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Gene symbol disambiguation using knowledge-based profiles
Bioinformatics
Inter-coder agreement for computational linguistics
Computational Linguistics
An unsupervised vector approach to biomedical term disambiguation: integrating UMLS and Medline
HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Graph connectivity measures for unsupervised word sense disambiguation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
DTMBIO 2011: international workshop on data and textmining in biomedical informatics
Proceedings of the 20th ACM international conference on Information and knowledge management
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Topic models are an established technique for generating information about the subjects discussed in collections of documents. Latent Dirichlet Allocation (LDA) is a widely applied topic model. The topic models generated by LDA consist of sets of terms associated with each topic and these are used to provide context for a Word Sense Disambiguation (WSD) system. It is found that using this context leads to a statistically significant improvement in the performance of a graph-based WSD system when applied to a standard evaluation resource.