Using Machine Learning to Maintain Rule-based Named-Entity Recognition and Classification Systems

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 rulebased systems.

• Type: Conference
• Year: 2001
• Where: ACL
• Authors: Georgios Petasis, Frantz Vichot, Francis Wolinski, Georgios Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos
• Keywords: ACL 2001, ACL 2007, Machine Learning, Rule-based Named-entity Recognition, Rule-based System