FACILE: classifying texts integrating pattern matching and information extraction

  • Authors:
  • Fabio Ciravegna;Alberto Lavelli;Nadia Mana;Johannes Matiasek;Luca Gilardoni;Silvia Mazza;Massimo Ferraro;William J. Black;Fabio Rinaldi;David Mowatt

  • Affiliations:
  • ITC-irst, Trento, Italy;ITC-irst, Trento, Italy;ITC-irst, Trento, Italy;OFAI, Vienna, Austria;Quinary SpA, Milan, Italy;Quinary SpA, Milan, Italy;Quinary SpA, Milan, Italy;UMIST, Manchester, United Kingdom;UMIST, Manchester, United Kingdom;UMIST, Manchester, United Kingdom

  • Venue:
  • IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1999

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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 multilingual textual data; therefore advanced classification 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.