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Using a morphological analyzer in high precision POS tagging of Hungarian
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC 2006)
Abstract
The paper presents an evaluation of maxent POS disambiguation systems that incorporate an open source morphological analyzer to constrain the probabilistic models. The experiments show that the best proposed architecture, which is the first application of the maximum entropy framework in a Hungarian NLP task, outperforms comparable state of the art tagging methods and is able to handle out of vocabulary items robustly, allowing for efficient analysis of large (web-based) corpora.