Paper: Named Entity Recognition Using Hundreds Of Thousands Of Features

ACL ID W03-0429
Title Named Entity Recognition Using Hundreds Of Thousands Of Features
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

We present an approach to named entity recog- nition that uses support vector machines to cap- ture transition probabilities in a lattice. The support vector machines are trained with hun- dreds of thousands of features drawn from the CoNLL-2003 Shared Task training data. Mar- gin outputs are converted to estimated prob- abilities using a simple static function. Per- formance 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 Ger- man.