Learning syntactic patterns using boosting and other classifier combination schemas

Authors:
András Hócza;László Felföldi;András Kocsor
Affiliations:
Department of Informatics, University of Szeged, Szeged;Department of Informatics, University of Szeged, Szeged;Department of Informatics, University of Szeged, Szeged
Venue:
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Year:
2005

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Abstract

This paper presents a method for the syntactic parsing of Hungarian natural language texts using a machine learning approach. This method learns tree patterns with various phrase types described by regular expressions from an annotated corpus. The PGS algorithm, an improved version of the RGLearn method, is developed and applied as a classifier in classifier combination schemas. Experiments show that classifier combinations, especially the Boosting algorithm, can effectively improve the recognition accuracy of the syntactic parser.