Paper: Good Neighbors Make Good Senses: Exploiting Distributional Similarity for Unsupervised WSD

ACL ID C08-1009
Title Good Neighbors Make Good Senses: Exploiting Distributional Similarity for Unsupervised WSD
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

We present an automatic method for sense- labeling of text in an unsupervised manner. The method makes use of distributionally similar words to derive an automatically labeled training set, which is then used to train a standard supervised classifier for distinguishing word senses. Experimental results on the Senseval-2 and Senseval-3 datasets show that our approach yields sig- nificant improvements over state-of-the-art unsupervised methods, and is competitive with supervised ones, while eliminating the annotation cost.