SVMTool: An Open Source generator of sequential taggers. The SVMTool is a simple and effective generator of sequential taggers based on Support Vector Machine. We have applied the SVMTool to a number of NLP problems, such as Part-of-speech Tagging and Base Phrase Chunking, for different languages. 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 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Turdakov, D.Yu.: Word sense disambiguation methods (2010)
- Daumé, Hal; Langford, John; Marcu, Daniel: Search-based structured prediction (2009)
- Ponzetto, S.P.; Strube, M.: Knowledge derived from wikipedia for computing semantic relatedness (2007)