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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 significant improvements over state-of-the-art unsupervised methods, and is competitive with supervised ones, while eliminating the annotation cost.
|Title of host publication||Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)|
|Publisher||Association for Computational Linguistics|
|Number of pages||8|
|Publication status||Published - 2008|