Article

Lattice-based tagging using support vector machines

Authors:

James Mayfield,

Paul McNamee,

Christine Piatko,

Claudia PearceAuthors Info & Claims

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

Pages 303 - 308

https://doi.org/10.1145/956863.956921

Published: 03 November 2003 Publication History

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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.

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Cited By

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Martinez A(2012)Part-of-speech taggingWIREs Computational Statistics10.1002/wics.1954:1(107-113)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1002/wics.195
Johnson Lim SLiu YLee W(2010)Multi-facet product information search and retrieval using semantically annotated product family ontologyInformation Processing and Management: an International Journal10.1016/j.ipm.2009.09.00146:4(479-493)Online publication date: 1-Jul-2010
https://dl.acm.org/doi/10.1016/j.ipm.2009.09.001
Lim SLiu YLee WAlonso OZaragoza H(2009)Faceted search and retrieval based on semantically annotated product family ontologyProceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval10.1145/1506250.1506254(15-24)Online publication date: 9-Feb-2009
https://dl.acm.org/doi/10.1145/1506250.1506254

Index Terms

Lattice-based tagging using support vector machines
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Information & Contributors

Information

Published In

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

November 2003

592 pages

ISBN:1581137230

DOI:10.1145/956863

General Chair:
Donald Kraft
Louisiana State University
,
Program Chairs:
Ophir Frieder
Illinois Institute of Technology
,
Joachim Hammer
University of Florida
,
Sajda Qureshi
University of Nebraska, Omaha
,
Len Seligman
The MITRE Corporation

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 03 November 2003

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November 3 - 8, 2003

LA, New Orleans, USA

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Cited By

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Martinez A(2012)Part-of-speech taggingWIREs Computational Statistics10.1002/wics.1954:1(107-113)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1002/wics.195
Johnson Lim SLiu YLee W(2010)Multi-facet product information search and retrieval using semantically annotated product family ontologyInformation Processing and Management: an International Journal10.1016/j.ipm.2009.09.00146:4(479-493)Online publication date: 1-Jul-2010
https://dl.acm.org/doi/10.1016/j.ipm.2009.09.001
Lim SLiu YLee WAlonso OZaragoza H(2009)Faceted search and retrieval based on semantically annotated product family ontologyProceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval10.1145/1506250.1506254(15-24)Online publication date: 9-Feb-2009
https://dl.acm.org/doi/10.1145/1506250.1506254

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Clinical entity recognition using structural support vector machines with rich features

Incremental training of support vector machines using hyperspheres

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