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Named Entity Recognition using Hundreds of Thousands of Features

We present an approach to named entity recognition that uses support vector machines to capture transition probabilities in a lattice. The support vector machines are trained with hundreds of thousands of features drawn from the CoNLL-2003 Shared Task training data. Margin outputs are converted to estimated probabilities using a simple static function. Performance is evaluated using the CoNLL-2003 Shared Task test set; Test B results were F=1 = 84.67 for English, and F=1 = 69.96 for German.


James Mayfield, Paul McNamee and Christine Piatko, Named Entity Recognition using Hundreds of Thousands of Features. In: Proceedings of CoNLL-2003, Edmonton, Canada, 2003, pp. 184-187. [ps] [ps.gz] [pdf] [bibtex]
Last update: June 11, 2003. erikt@uia.ua.ac.be