Using machine learning to maintain rule-based named-entity recognition and classification systems

Authors:
Georgios Petasis;Frantz Vichot;Francis Wolinski;Georgios Paliouras;Vangelis Karkaletsis;Constantine D. Spyropoulos
Affiliations:
National Centre for Scientific Research "Demokritos", Athens, Greece;Informatique-CDC, rue Berthollet, Arcueil, France;Informatique-CDC, rue Berthollet, Arcueil, France;National Centre for Scientific Research "Demokritos", Athens, Greece;National Centre for Scientific Research "Demokritos", Athens, Greece;National Centre for Scientific Research "Demokritos", Athens, Greece
Venue:
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Year:
2001

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Abstract

This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, constructed with the use of machine learning, to monitor the performance of the rule-based system. The training data for the second system is generated with the use of the rule-based system, thus avoiding the need for manual tagging. The disagreement of the two systems acts as a signal for updating the rule-based system. The generality of the approach is illustrated by applying it to large corpora in two different languages: Greek and French. The results are very encouraging, showing that this alternative use of machine learning can assist significantly in the maintenance of rule-based systems.