Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40533
Title: Scaling up word sense disambiguation via parallel texts
Authors: Chan, Y.S. 
Ng, H.T. 
Issue Date: 2005
Source: Chan, Y.S.,Ng, H.T. (2005). Scaling up word sense disambiguation via parallel texts. Proceedings of the National Conference on Artificial Intelligence 3 : 1037-1042. ScholarBank@NUS Repository.
Abstract: A critical problem faced by current supervised WSD systems is the lack of manually annotated training data. Tackling this data acquisition bottleneck is crucial, in order to build high-accuracy and wide-coverage WSD systems. In this paper, we show that the approach of automatically gathering training examples from parallel texts is scalable to a large set of nouns. We conducted evaluation on the nouns of SENSEVAL-2 English all-words task, using fine-grained sense scoring. Our evaluation shows that training on examples gathered from 680MB of parallel texts achieves accuracy comparable to the best system of SENSEVAL-2 English all-words task, and significantly outperforms the baseline of always choosing sense 1 of WordNet. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
Source Title: Proceedings of the National Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/40533
Appears in Collections:Staff Publications

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