HERMIT: Flexible clustering for the SemEval-2 WSI task
Proceedings of the 5th International Workshop on Semantic Evaluation, page 359--362. Stroudsburg, PA, USA, Association for Computational Linguistics, (2010)Abstract
A single word may have multiple unspecified meanings in a corpus. Word sense induction aims to discover these different meanings through word use, and knowledge-lean algorithms attempt this without using external lexical resources. We propose a new method for identifying the different senses that uses a flexible clustering strategy to automatically determine the number of senses, rather than predefining it. We demonstrate the effectiveness using the SemEval-2 WSI task, achieving competitive scores on both the V-Measure and Recall metrics, depending on the parameter configuration.
