Summary of the paper
| Title | Learning Morphology with Morfette |
|---|---|
| Authors | Grzegorz Chrupala, Georgiana Dinu and Josef van Genabith |
| Abstract | 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 word forms 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. |
| Language | Language-independent |
| Topics | Morphology, Acquisition, Machine Learning, Statistical methods |
Full paper |
Learning Morphology with Morfette |
Slides |
Learning Morphology with Morfette |
| Bibtex | @InProceedings{CHRUPALA08.594, author = {Grzegorz Chrupala, Georgiana Dinu and Josef van Genabith}, title = {Learning Morphology with Morfette}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |