A simple, flexible, and effective generator of sequential taggers based on Support Vector Machines. We have appied the SVMTool to the problem of part-of-speech tagging. By means of a rigorous experimental evaluation, we conclude that the proposed SVM-based tagger is robust and flexible for feature modelling (including lexicalization), trains efficiently with almost no parameters to tune, and is able to tag thousands of words per second, which makes it really practical for real NLP applications. Regarding accuracy, the SVM-based tagger significantly outperforms the TnT tagger exactly under the same conditions, and achieves a very competitive accuracy of 97.2% for English on the Wall Street Journal corpus, which is comparable to the best taggers reported up to date. It has been also successfully applied to Spanish and Catalan exhibiting a similar performance, and to other tagging problems such as base phrase chunking.

Perl / C++

SVM Learning, SVM Tagger

Fast and accurate (state-of-the-art) part-of-speech tagging.

• Jesús Giménez and Lluís Màrquez. Fast and Accurate Part-of-Speech Tagging: The SVM Approach Revisited. In Proceedings of the International Conference RANLP – 2003 (Recent Advances in Natural Language Processing), pages 158 – 165. September, 10-12, 2003. Borovets, Bulgary. (ISBN 954-90906-6-3). Selected as a chapter in RANLP 2003 volume in CILT series (Current Issues in Linguistic Theory). John Benjamins Publishers, Amsterdam.
• Jesús Giménez and Lluís Màrquez. SVMTool: A general POS tagger generator based on Support Vector Machines. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC’04), vol. I, pages 43 – 46. Lisbon, Portugal, 2004. (ISBN 2-9517408-1-6). Departament Research Report (LSI-04-34-R), Technical University of Catalonia, 2004.