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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

Lafferty et al., 2001, Conditional Random Fields (Proceedings of the 18th International Conference on Machine Learning (ICML))

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6 Further aspects of CRFs

  1. What extensions of CRFs did the paper suggest?
    Training with AdaBoost’s exponential loss over whole sequences (using forward–backward for feature expectations), feature selection and induction (adapting Della Pietra et al.’s methods), and tree-structured or grammar-like CRFs (analogues of stochastic context-free grammars).
    • Lafferty et al., 2001, Conditional Random Fields (Proceedings of the 18th International Conference on Machine Learning (ICML)), §6

Source

  • John Lafferty, Andrew McCallum and Fernando C. N. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the 18th International Conference on Machine Learning (ICML), pp. 282–289.