Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41983
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dc.titleWord sense disambiguation using OntoNotes: An empirical study
dc.contributor.authorZhong, Z.
dc.contributor.authorNg, H.T.
dc.contributor.authorChan, Y.S.
dc.date.accessioned2013-07-04T08:40:29Z
dc.date.available2013-07-04T08:40:29Z
dc.date.issued2008
dc.identifier.citationZhong, Z., Ng, H.T., Chan, Y.S. (2008). Word sense disambiguation using OntoNotes: An empirical study. EMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL : 1002-1010. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41983
dc.description.abstractThe accuracy of current word sense disambiguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD examples provided through OntoNotes, we conduct the first large-scale WSD evaluation involving hundreds of word types and tens of thousands of sense-tagged examples, while adopting a coarse-grained sense inventory. We show that though WSD systems trained with a large number of examples can obtain a high level of accuracy, they nevertheless suffer a substantial drop in accuracy when applied to a different domain. To address this issue, we propose combining a domain adaptation technique using feature augmentation with active learning. Our results show that this approach is effective in reducing the annotation effort required to adapt a WSD system to a new domain. Finally, we propose that one can maximize the dual benefits of reducing the annotation effort while ensuring an increase in WSD accuracy, by only performing active learning on the set of most frequently occurring word types. © 2008 Association for Computational Linguistics.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.sourcetitleEMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL
dc.description.page1002-1010
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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