Lattice-based tagging using support vector machines

Authors:
James Mayfield;Paul McNamee;Christine Piatko;Claudia Pearce
Affiliations:
The Johns Hopkins University, Laurel, MD;The Johns Hopkins University, Laurel, MD;The Johns Hopkins University, Laurel, MD;Department of Defense, Ft. Meade, MD
Venue:
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Year:
2003

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Abstract

Tagging algorithms have become increasingly important for identifying lexical and semantic features of unstructured text. We describe an approach to lattice-based tagging that estimates joint transition and emission probabilities using support vector machines. The technique offers several advantages over alternative methods, including the ability to accommodate non-local features, support for hundreds of thousands of features, and language-neutrality. We demonstrate the technique on two tagging applications: named entity recognition and part-of-speech tagging.