Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/42004
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dc.titleOther-anaphora resolution in biomedical texts with automatically mined patterns
dc.contributor.authorChen, B.
dc.contributor.authorYang, X.
dc.contributor.authorJian, S.
dc.contributor.authorLim, T.C.
dc.date.accessioned2013-07-04T08:40:59Z
dc.date.available2013-07-04T08:40:59Z
dc.date.issued2008
dc.identifier.citationChen, B.,Yang, X.,Jian, S.,Lim, T.C. (2008). Other-anaphora resolution in biomedical texts with automatically mined patterns. Coling 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference 1 : 121-128. ScholarBank@NUS Repository.
dc.identifier.isbn9781905593446
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42004
dc.description.abstractThis paper proposes an other-anaphora resolution approach in bio-medical texts. It utilizes automatically mined patterns to discover the semantic relation between an anaphor and a candidate antecedent. The knowledge from lexical patterns is incorporated in a machine learning framework to perform anaphora resolution. The experiments show that machine learning approach combined with the auto-mined knowledge is effective for other-anaphora resolution in the biomedical domain. Our system with auto-mined patterns gives an accuracy of 56.5%., yielding 16.2% improvement against the baseline system without pattern features, and 9% improvement against the system using manually designed patterns. © 2008 Licensed under the Creative Commons.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
dc.description.volume1
dc.description.page121-128
dc.identifier.isiutNOT_IN_WOS
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