Please use this identifier to cite or link to this item:
|Title:||Scaling up word sense disambiguation via parallel texts|
|Authors:||Chan, Y.S. |
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.