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

@inproceedings{Lafferty2001ConditionalRF,
  title={Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data},
  author={John D. Lafferty and Andrew McCallum and Fernando Pereira},
  booktitle={ICML},
  year={2001}
}
We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models… CONTINUE READING
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