Graph Algorithms for Word Sense Disambiguation in Biomedicine | Zendy

Rodrigo Goulart | Zendy; Juliano De Carvalho | Zendy; Vera De Lima | Zendy

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Graph Algorithms for Word Sense Disambiguation in Biomedicine

Author(s) -

Rodrigo Goulart,

Juliano De Carvalho,

Vera De Lima

Publication year - 2015

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5753/sbcas.2015.10365

Subject(s) - word sense disambiguation , biomedicine , computer science , graph , natural language processing , artificial intelligence , word (group theory) , task (project management) , semeval , point (geometry) , algorithm , machine learning , theoretical computer science , mathematics , genetics , geometry , management , wordnet , economics , biology

Word Sense Disambiguation (WSD) is an important task for Biomedicine text-mining. Supervised WSD methods have the best results but they are complex and their cost for testing is too high. This work presents an experiment on WSD using graph-based approaches (unsupervised methods). Three algorithms were tested and compared to the state of the art. Results indicate that similar performance could be reached with different levels of complexity, what may point to a new approach to this problem.

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