Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly in problems with large numbers of potential input features. This paper describes a new method for training CRFs by applying Friedman's (1999) gradient tree boosting method. In tree boosting, the CRF potential functions are represented as weighted sums of regression trees. Regression trees are learned by stage-wise optimizations similar to Adaboost, but with the objective of maximizing the conditional likelihood P(Y|X) of the CRF model. By growing regression trees, interactions among features are introduced only as needed, so although the parameter space is potentially immense, the search algorithm does not explicitly consider the large space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially like previous algorithms based on iterative scaling and gradient descent.

REFERENCES

• 1: Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society B, 36, 192--236.
• 2: Besag, J. (1977). Efficiency of pseudolikelihood estimation for simple Gaussian fields. Biometrika, 64, 616--618.
• 3: Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth International Group.
• 4: Thomas G. Dietterich, Machine Learning for Sequential Data: A Review, Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, p.15-30, August 06-09, 2002
• 5: Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. ICML-96 (pp. 148--156). Morgan Kaufmann.
• 6: Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29.
• 7: Geman, D. (1998). Random fields and inverse problems in imaging. In A. Ancona, D. Geman and N. Ikeda (Eds.), École d'Été de probabilités de saintflour xviii, Lecture Notes in Mathematics 1427, 117--196. Berlin: Springer-Verlag.
• 8: Hammersley, J. M., & Clifford, P. (1971). Markov fields on finite graphs and lattices (Technical Report). Unpublished.
• 9: John D. Lafferty , Andrew McCallum , Fernando C. N. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning, p.282-289, June 28-July 01, 2001
• 10: McCallum, A. (2003). Efficiently inducing features of conditional random fields. UAI-2003 (pp. 403--410). San Francisco, CA: Morgan Kaufmann.
• 11: Andrew McCallum , Dayne Freitag , Fernando C. N. Pereira, Maximum Entropy Markov Models for Information Extraction and Segmentation, Proceedings of the Seventeenth International Conference on Machine Learning, p.591-598, June 29-July 02, 2000
• 12: Qian, N., & Sejnowski, T. J. (1988). Predicting the secondary structure of globular proteins using neural network models. J. Mol. Bio., 202, 865--884.
• 13: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 14: Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77, 257--286.
• 15: Ratnaparkhi, A. (1996). A maximum entropy model for part-of-speech tagging. Proceedings of the conference on empirical methods in natural language processing (pp. 133--142). Somerset, NJ: ACL.
• 16: Sejnowski, T. J., & Rosenberg, C. R. (1987). Parallel networks that learn to pronouce English text. Complex Systems, 1, 145--168.