Learning syntactic tree patterns from a balanced Hungarian natural language database, the Szeged Treebank

The current paper has a twofold objective. On the one hand, it describes the creation and the features of the Szeged Treebank, which is currently the largest manually processed Hungarian textual database serving as a reference material for research in natural language processing. On the other hand, detailed information is given about different experiments that aimed at the automatic recognition of syntactic structures with the use of machine learning algorithms. In order to provide comparable results, we applied methods of different categories, namely a rule-based, a logic and a numeric learner to pre-defined parsing problems. The aforementioned Szeged Treebank was used for the training and the testing of the algorithms.