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.
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Hócza, A., Felföldi, L., Kocsor, A. (2005). Learning Syntactic Patterns Using Boosting and Other Classifier Combination Schemas. In: Matoušek, V., Mautner, P., Pavelka, T. (eds) Text, Speech and Dialogue. TSD 2005. Lecture Notes in Computer Science(), vol 3658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551874_9
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DOI: https://doi.org/10.1007/11551874_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28789-6
Online ISBN: 978-3-540-31817-0
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