Named entity recognition using hundreds of thousands of features

Authors:
James Mayfield;Paul McNamee;Christine Piatko
Affiliations:
The Johns Hopkins University, Laurel, Maryland;The Johns Hopkins University, Laurel, Maryland;The Johns Hopkins University, Laurel, Maryland
Venue:
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Year:
2003

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Abstract

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 Fβ=1 = 84.67 for English, and Fβ=1 = 69.96 for German.