Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40533
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dc.titleScaling up word sense disambiguation via parallel texts
dc.contributor.authorChan, Y.S.
dc.contributor.authorNg, H.T.
dc.date.accessioned2013-07-04T08:06:31Z
dc.date.available2013-07-04T08:06:31Z
dc.date.issued2005
dc.identifier.citationChan, 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.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40533
dc.description.abstractA 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.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the National Conference on Artificial Intelligence
dc.description.volume3
dc.description.page1037-1042
dc.description.codenPNAIE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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