Lattice-based tagging using support vector machines

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.

DOI: 10.1145/956863.956921

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@inproceedings{Mayfield2003LatticebasedTU, title={Lattice-based tagging using support vector machines}, author={James Mayfield and Paul McNamee and Christine D. Piatko and Claudia Pearce}, booktitle={CIKM}, year={2003} }