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dc.contributor.advisorSteedman, Marken
dc.contributor.authorDilg, Fraukeen
dc.date.accessioned2012-07-06T13:01:30Z
dc.date.available2012-07-06T13:01:30Z
dc.date.issued2011-08-31
dc.identifier.urihttp://hdl.handle.net/1842/6090
dc.description.abstractThe project is about learning temporal relations from unannotated text. This effort builds on the work of Lapata M. and Lascarides, A. (2006): Learning sentence-internal temporal relations, who developed a system that uses temporal connectors (after, before, while, when, as, once, until and since) in unannotated text to build a system to determine intra-sentential temporal relations. In an extension of this approach, they used their system to determine TimeML relations (before, includes, begins, ends and simultaneous) between events. Since temporal connectors do not translate one-to-one to TimeML relations, the main focus of this project is on disambiguating the temporal connectors into TimeML relations to preprocess the training data and use the system to directly learn the TimeML relations. This is done using a rule-based system and evaluated on the TimeBank corpus.en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.subjectComputational Semanticsen
dc.subjectTemporal Evaluationen
dc.subjectTimeMLen
dc.subjectTemporal Semanticsen
dc.titleDisambiguating Temporal Connectors into TimeML relationsen
dc.typeThesis or Dissertationen
dc.relation.referencesLapata, M. and Lascarides, A. (2006). Learning sentence-internal temporal relations. Journal of Artificial Intelligence Research, 27(1):85-117en
dc.relation.referencesDorr, B.J. and Gaasterland, T. (2007). Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction. Information Processing and Management, 43(6):1681-1704en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc Master of Scienceen
dcterms.accessRightsRestricted Accessen


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