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Comparisons of sequence labeling algorithms and extensions

Published: 20 June 2007 Publication History

Abstract

In this paper, we survey the current state-of-art models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron (AP), Structured SVMs (SVMstruct), Max Margin Markov Networks (M3N), and an integration of search and learning algorithm (SEARN). With all due tuning efforts of various parameters of each model, on the data sets we have applied the models to, we found that SVMstruct enjoys better performance compared with the others. In addition, we also propose a new method which we call the Structured Learning Ensemble (SLE) to combine these structured learning models. Empirical results show that our SLE algorithm provides more accurate solutions compared with the best results of the individual models.

References

[1]
Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004). Ensemble selection from libraries of models. Proceedings of the 21st International Conference on Machine Learning.
[2]
Collins, M. (2002). Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. EMNLP.
[3]
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Joural of Machine Learning Research, 2, 265--292.
[4]
Daumé III, H., Langford, J., & Marcu, D. (2006). Searchbased structured prediction. Submitted to the Machine Learning Journal.
[5]
Kassel, R. (1995). A comparison of approaches on online handwritten character recognition. Ph.D. Thesis, MIT Spoken Language System Group.
[6]
Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. International Conference on Machine Learning.
[7]
McCallum, A. K. (2002). Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.
[8]
Murphy, K. (1998). Hidden markov model (hmm) toolbox for matlab. http://www.cs.ubc.ca/murphyk/Software/HMM/hmm.html.
[9]
Peng, F., & McCallum, A. (2004). Accurate information extraction from research papers using conditional random fields. HLT/NAACL.
[10]
Platt, J. C. (1999). Using analytic qp and sparseness to speed training of support vector machines. Proceedings of Advances in neural information processing systems (pp. 557--563).
[11]
Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE.
[12]
Seymore, K., McCallum, A., & Rosenfeld, R. (1999). Learning hidden Markov model structure for information extraction. AAAI 99 Workshop on Machine Learning for Information Extraction.
[13]
Takasu, A. (2003). Bibliographic attribute extraction from erroneous references based on a statistical model. JCDL '03: Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries.
[14]
Taskar, B., Guestrin, C., & Koller, D. (2003). Maxmargin markov networks. Advances in Neural Information Processing System 16.
[15]
The Penn Treebank (2002). Penn's linguistic data consortium. http://www.cis.upenn.edu/treebank.
[16]
Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Joural of Machine Learning Research, 6, 1453--1484.

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    cover image ACM Other conferences
    ICML '07: Proceedings of the 24th international conference on Machine learning
    June 2007
    1233 pages
    ISBN:9781595937933
    DOI:10.1145/1273496
    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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    Published: 20 June 2007

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