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  Large Margin Methods for Part‐of‐Speech Tagging

Altun, Y. (2009). Large Margin Methods for Part‐of‐Speech Tagging. In J. Keshet, & S. Bengio (Eds.), Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods (pp. 139-158). Hoboken, NJ, USA: Wiley.

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 Creators:
Altun, Y1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Part of speech tagging, an important component of speech recognition systems, is a sequence labeling problem which involves in- ferring a state sequence from an observation sequence, where the state sequence encodes a labeling, annotation or segmentation of an observa- tion sequence. In this paper we give an overview of discriminative meth- ods developed for this problem. Special emphasis is put on large margin methods by generalizing multiclass Support Vector Machines and Ad- aBoost to the case of label sequences. Experimental evaluation on Part of Speech Tagging demonstrates the advantages of these models over clas- sical approaches like Hidden Markov Models and their competitiveness with methods like Conditional Random Fields.

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 Dates: 2009-01
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 5367
DOI: 10.1002/9780470742044.ch9
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Title: Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
Source Genre: Book
 Creator(s):
Keshet, J, Editor
Bengio, S, Editor
Affiliations:
-
Publ. Info: Hoboken, NJ, USA : Wiley
Pages: - Volume / Issue: - Sequence Number: 9 Start / End Page: 139 - 158 Identifier: ISBN: 978-0-470-69683-5