Corpus ID: 219683473

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

  title={Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data},
  author={J. Lafferty and A. McCallum and F. Pereira},
  • J. Lafferty, A. McCallum, F. Pereira
  • Published in ICML 2001
  • Computer Science
  • 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
    Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
    • 2,884
    • Open Access
    Shallow Parsing with Conditional Random Fields
    • 1,493
    • Highly Influenced
    • Open Access
    Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
    • 2,185
    • Open Access
    Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
    • 866
    • Open Access
    Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
    • 2,059
    • Open Access
    Natural Language Processing (Almost) from Scratch
    • 5,429
    • Open Access
    A Markov random field model for term dependencies
    • 902
    • Open Access
    Semi-Markov Conditional Random Fields for Information Extraction
    • 654
    • Open Access
    Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
    • 1,068
    • Open Access


    Publications referenced by this paper.
    Gradient-based learning applied to document recognition
    • 24,118
    • Open Access
    Maximum Entropy Markov Models for Information Extraction and Segmentation
    • 1,464
    • Open Access
    A decision-theoretic generalization of on-line learning and an application to boosting
    • 11,004
    • Open Access
    Inducing Features of Random Fields
    • 1,258
    • Open Access
    Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
    • 4,222
    • Open Access
    Foundations of statistical natural language processing
    • 6,233
    • Open Access
    A Maximum Entropy Approach to Natural Language Processing
    • 3,288
    • Open Access
    Efficient Training of Conditional Random Fields
    • 175
    • Open Access
    Information Extraction with HMM Structures Learned by Stochastic Optimization
    • 308
    • Open Access
    GradientBased Learning Applied to Document Recognition
    • 2,595