Learning morphology with Morfette
Chrupała, Grzegorz and Dinu, Georgiana and van Genabith, Josef (2008) Learning morphology with Morfette. In: LREC 2008 - Sixth International Conference on Language Resources and Evaluation, 28-30 May 2008, Marrakech, Morocco.
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Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from wordforms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific
feature engineering or additional resources.
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