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dc.contributor.authorCrowe, Jeremy David Macdonalden
dc.date.accessioned2019-02-15T14:15:00Z
dc.date.available2019-02-15T14:15:00Z
dc.date.issued1997en
dc.identifier.urihttp://hdl.handle.net/1842/33385
dc.description.abstracten
dc.description.abstractA common feature of news reports is the reference to events other than the one which is central to the discourse. Previous research has suggested Gricean explanations for this; more generally, the phenomenon has been referred to simply as "journalistic style". Whatever the underlying reasons, recent investigations into information extraction have emphasised the need for a better understanding of the mechanisms that can be used to recognise and distinguish between multiple events in discourse.en
dc.description.abstractExisting information extraction systems approach the problem of event recognition in a number of ways. However, although frameworks and techniques for black box evaluations of information extraction systems have been developed in recent years, almost no attention has been given to the evaluation of techniques for event recognition, despite general acknowledgment of the inadequacies of current implementations. Not only is it unclear which mechanisms are useful, but there is also little consensus as to how such mechanisms could be compared.en
dc.description.abstractThis thesis presents a formalism for representing event structure, and introduces an evaluation metric through which a range of event recognition mechanisms are quantitatively compared. These mechanisms are implemented as modules within the CONTESS event recognition system, and explore the use of linguistic phenomena such as temporal phrases, locative phrases and cue phrases, as well as various discourse structuring heuristics.en
dc.description.abstractOur results show that, whilst temporal and cue phrases are consistently useful in event recognition, locative phrases are better ignored. A number of further linguistic phenomena and heuristics are examined, providing an insight into their value for event recognition purposes.en
dc.publisherThe University of Edinburghen
dc.relation.ispartofAnnexe Thesis Digitisation Project 2019 Block 22en
dc.relation.isreferencedbyAlready catalogueden
dc.titleConstraint based event recognition for information extractionen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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