An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
HLT '93 Proceedings of the workshop on Human Language Technology
Understanding the Yarowsky Algorithm
Computational Linguistics
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
SemEval-2007 task 01: evaluating WSD on cross-language information retrieval
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Topic models for word sense disambiguation and token-based idiom detection
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Semantic topic models: combining word distributional statistics and dictionary definitions
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We extend on McCarthy et al.'s predominant sense method to create an unsupervised method of word sense disambiguation that uses automatically derived topics using Latent Dirichlet allocation. Using topic-specific synset similarity measures, we create predictions for each word in each document using only word frequency information. It is hoped that this procedure can improve upon the method for larger numbers of topics by providing more relevant training corpora for the individual topics. This method is evaluated on SemEval-2007 Task 1 and Task 17.