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Extending nocuous ambiguity analysis for anaphora in natural language requirements

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dc.contributor.author Yang, Hui
dc.contributor.author De Roeck, Anne
dc.contributor.author Gervasi, Vincenzo
dc.contributor.author Willis, Alistair
dc.contributor.author Nuseibeh, Bashar
dc.date.accessioned 2011-02-04T12:12:06Z
dc.date.available 2011-02-04T12:12:06Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/10344/732
dc.description peer-reviewed en_US
dc.description.abstract This paper presents an approach to automatically identify potentially nocuous ambiguities, which occur when text is interpreted differently by different readers of requirements written in natural language. We extract a set of anaphora ambiguities from a range of requirements documents, and collect multiple human judgments on their interpretations. The judgment distribution is used to determine if an ambiguity is nocuous or innocuous. We investigate a number of antecedent preference heuristics that we use to explore aspects of anaphora which may lead a reader to favour a particular interpretation. Using machine learning techniques, we build an automated tool to predict the antecedent preference of noun phrase candidates, which in turn is used to identify nocuous ambiguity. We report on a series of experiments that we conducted to evaluate the performance of our automated system. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries 18th IEEE International Requirements Engineering Conference, 2010;pp.25-34
dc.relation.uri http://dx.doi.org/10.1109/RE.2010.14
dc.rights ©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. en_US
dc.subject nocuous ambiguity en_US
dc.subject anaphora ambiguity en_US
dc.subject natural language en_US
dc.subject antecedent preference heuristics en_US
dc.subject machine learning en_US
dc.title Extending nocuous ambiguity analysis for anaphora in natural language requirements 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 EPSRC
dc.relation.projectid 03/CE2/I303_1
dc.relation.projectid MaTREx project (EP/F068859/1)
dc.internal.authorcontactother Bashar.Nuseibeh@lero.ie


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