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Abstract:In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text. We combine word representations created on large corpora with a small number of definitions from the UMLS to create concept representations, which we then compare to representations of the context of ambiguous terms. Using no relational information, we obtain comparable performance to previous approaches on the MSH-WSD dataset, which is a well-known dataset in the biomedical domain. Additionally, our method is fast and easy to set up and extend to other domains. Supplementary materials, including source code, can be found at https: //github.com/clips/yarn
* Proceedings of the 15th Workshop on Biomedical Natural Language
Processing, Berlin, Germany, 2016, pages 77-82. Association for Computational
Linguistics * 6 pages, 1 figure, presented at the 15th Workshop on Biomedical
Natural Language Processing, Berlin 2016