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Title: Constraint based event recognition for information extraction
Author: Crowe, J. D. M.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1996
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A 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. Existing 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 acknowledgement 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. This 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 systems, and explore the use of linguistic phenomena such as temporal phrases, locative phrases and cue phrases, as well as various discourse structuring heuristics. Our 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available