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Tech Report kmi-04-14 Abstract


Event Recognition on News Stories and Semi-Automatic Population of an Ontology
Techreport ID: kmi-04-14
Date: 2004
Author(s): Maria Vargas-Vera, David Celjuska
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This paper describes a system which recognizes events on news stories. Our system classifies stories and populates a hand-crafted ontology with new instances of classes defined in it. Currently, our system recognizes events which can be classified as belonging to a single category and it also recognizes overlapping events within one article (more than one event is recognized). In each case, the system provides a confidence value associated to the suggested classification. Our system uses Information Extraction and Machine Learning technologies. The system was tested using a corpus of 200 news articles from an archive of electronic news stories describing the academic life of the Knowledge Media (KMi). In particular, these news stories describe events such as a project award, publications, visits, etc.
 
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New Media Systems is...


Our New Media Systems research theme aims to show how new media devices, standards, architectures and concepts can change the nature of learning.

Our work involves the development of short life-cycle working prototypes of innovative technologies or concepts that we believe will influence the future of open learning within a 3-5 year timescale. Each new media concept is built into a working prototype of how the innovation may change a target community. The working prototypes are all available (in some form) from this website.

Our prototypes themselves are not designed solely for traditional Open Learning, but include a remit to show how that innovation can and will change learning at all levels and in all forms; in education, at work and play.