Adapting a Robust Multi-genre NE System for Automatic Content Extraction
Identifieur interne : 001826 ( Main/Exploration ); précédent : 001825; suivant : 001827Adapting a Robust Multi-genre NE System for Automatic Content Extraction
Auteurs : Diana Maynard [Royaume-Uni] ; Hamish Cunningham [Royaume-Uni] ; Kalina Bontcheva [Royaume-Uni] ; Marin Dimitrov [Bulgarie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: Many current information extraction systems tend to be designed with particular applications and domains in mind. With the increasing need for robust language engineering tools which can handle a variety of language processing demands, we have used the GATE architecture to design MUSE - a system for named entity recognition and related tasks. In this paper, we address the issue of how this general-purpose system can be adapted for particular applications with minimal time and effort, and how the set of resources used can be adapted dynamically and automatically. We focus specifically on the challenges of the ACE (Automatic Content Extraction) entity detection and tracking task, and preliminary results show promising figures.
Url:
DOI: 10.1007/3-540-46148-5_27
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: Many current information extraction systems tend to be designed with particular applications and domains in mind. With the increasing need for robust language engineering tools which can handle a variety of language processing demands, we have used the GATE architecture to design MUSE - a system for named entity recognition and related tasks. In this paper, we address the issue of how this general-purpose system can be adapted for particular applications with minimal time and effort, and how the set of resources used can be adapted dynamically and automatically. We focus specifically on the challenges of the ACE (Automatic Content Extraction) entity detection and tracking task, and preliminary results show promising figures.</div>
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