FACILE: Classifying Texts Integrating Pattern Matching and Information Retrieval

Fabio Ciravegna
Alberto Lavelli
Nadia Mana		 Johannes Matiasek Luca Gilardoni
Silvia Mazza
Massimo Ferraro 		William J. Black
Fabio Rinaldi
David Mowatt
ITC-irst
Loc. Pantè di Povo
38050 Trento Italy 		ÖFAI
Schottengasse 3
1010 Vienna Austria		 Quinary SpA
Via Fara 35
20124 Milan Italy		 UMIST - PO Box 88
Manchester M60 1QD
United Kingdom

Abstract

Successfully managing information means being able to find relevant new information and to correctly integrate it with pre-existing knowledge. Much information is nowadays stored as multi-lingual textual data; therefore advanced classifi-cation systems are currently considered as strategic components for effective knowledge management. We describe an experience integrating different innovative AI technologies such as hierarchical pattern matching and information extraction to provide flexible multilingual classification adaptable to user needs. Pattern matching produces fairly accurate and fast categorisation over a large number of classes, while information extraction provides fine-grained classification for a reduced number of classes. The resulting system was adopted by the main Italian financial news agency providing a pay-to-view service.

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Citation:

Fabio Ciravegna, Alberto Lavelli, Nadia Mana, Johannes Matiasek, Luca Gilardoni, Silvia Mazza, Massimo Ferraro , William J. Black, Fabio Rinaldi and David Mowatt: FACILE: Classifying Texts Integrating Pattern Matching and Information Retrieval. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 31 July - 6 August 1999. to appear.
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Johannes Matiasek
Last modified: Thu Apr 1 18:52:07 MET DST 1999