Comparisons of sequence labeling algorithms and extensions

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 681-688

  Nguyen, Nam ^a   Guo, Yunsong ^a  

^a School of Operations Research and Information Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS; LEARNING ALGORITHMS; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

AVERAGED PERCEPTRON; CONDITIONAL RANDOM FIELDS; SEQUENCE LABELING; STRUCTURED LEARNING ENSEMBLE;

STRUCTURED PROGRAMMING;

EID: 34547982896     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273582     Document Type: Conference Paper

References (16)

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 perception 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). Search-based 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. AAAI99 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.