The Journal of Machine Learning Research
NUS-ML: improving word sense disambiguation using topic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
TKB-UO: using sense clustering for WSD
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2010 task 14: evaluation setting for word sense induction & disambiguation systems
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
SemEval-2010 task 14: Word sense induction & disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Measuring the impact of sense similarity on word sense induction
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Evaluating Word Sense Induction and Disambiguation Methods
Language Resources and Evaluation
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We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution of the test instances. By clustering these topics distributions in their topic space we cluster them into different senses. Our hypothesis is that closeness in topic space reflects similarity between different word senses. This system participated in SemEval-2 word sense induction and disambiguation task and achieved the second highest V-measure score among all other systems.