Learning Syntactic Patterns Using Boosting and
Other Classifier Combination Schemas

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.