Cost-benefit methodology for office systems
ACM Transactions on Information Systems (TOIS)
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Human Language Technology for Automatic Annotation and Indexing of Digital Library Content
ECDL '02 Proceedings of the 6th European Conference on Research and Advanced Technology for Digital Libraries
Using a text engineering framework to build an extendable and portable IE-based summarisation system
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Introduction to information extraction
AI Communications
Towards a cultural heritage digital library
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
NE recognition without training data on a language you don't speak
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
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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.