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A methodology for automatic identification of nocuous ambiguity

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dc.contributor.author Yang, Hui
dc.contributor.author De Roeck, Anne
dc.contributor.author Willis, Alistair
dc.contributor.author Nuseibeh, Bashar
dc.date.accessioned 2011-02-04T12:25:57Z
dc.date.available 2011-02-04T12:25:57Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/10344/743
dc.description peer-reviewed en_US
dc.description.abstract Nocuous ambiguity occurs when a linguistic expression is interpreted differently by different readers in a given context. We present an approach to automatically identify nocuous ambiguity that is likely to lead to misunderstandings among readers. Our model is built on a machine learning architecture. It learns from a set of heuristics each of which predicts a factor that may lead a reader to favor a particular interpretation. An ambiguity threshold indicates the extent to which ambiguity can be tolerated in the application domain. Collections of human judgments are used to train heuristics and set ambiguity thresholds, and for evaluation. We report results from applying the methodology to coordination and anaphora ambiguity. Results show that the method can identify nocuous ambiguity in text, and may be widened to cover further types of ambiguity. We discuss approaches to evaluation. en_US
dc.language.iso eng en_US
dc.publisher The Association for Computational Linguistics en_US
dc.relation.ispartofseries Coling 2010, The 23rd International Conference on Computational Linguistics, 23-27 Aug 2010, Beijing, China;pp. 1218-1226
dc.relation.uri http://portal.acm.org/citation.cfm?id=1873918&CFID=8787620&CFTOKEN=92835322
dc.subject nocuous ambiguity en_US
dc.title A methodology for automatic identification of nocuous ambiguity en_US
dc.type Conference item en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.restriction none en
dc.contributor.sponsor SFI
dc.contributor.sponsor UK EPSRC
dc.relation.projectid 03/CE2/I303_1
dc.relation.projectid project MaTREx (EP/F068859/1)
dc.internal.authorcontactother Bashar.Nuseibeh@lero.ie


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