HERMIT: Flexible clustering for the SemEval-2 WSI task

Authors:
David Jurgens;Keith Stevens
Affiliations:
University of California, Los Angeles, California;University of California, Los Angeles, California
Venue:
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Year:
2010

Citing 3
Cited 2

Dependency-Based Construction of Semantic Space Models

Computational Linguistics
The S-Space package: an open source package for word space models

ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
SemEval-2010 task 14: Word sense induction & disambiguation

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation

The S-Space package: an open source package for word space models

ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Evaluating Word Sense Induction and Disambiguation Methods

Language Resources and Evaluation

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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.