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Learning flexible features for conditional random fields.

Liam Stewart1, Xuming He, Richard S Zemel

  • 1Google, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. liam@cs.toronto.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 21, 2008
PubMed
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This study introduces a novel model for structured data labeling that avoids exponential complexity by using parameterized features. This approach effectively learns complex label structures for tasks like information extraction and image labeling.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Traditional discriminative labeling models struggle with higher-order label structures, leading to increased complexity.
  • Existing methods face challenges in efficiently learning complex dependencies within structured data.

Purpose of the Study:

  • To present a new model capable of learning higher-order structures in labels for discriminative labeling tasks.
  • To address the issue of exponentially increasing model complexity in traditional approaches.
  • To introduce an automatic feature learning scheme for adaptive model complexity.

Main Methods:

  • Developed a model utilizing a random field of parameterized features.
  • Features are designed as functions of observations, labels, and auxiliary hidden variables.

Related Experiment Videos

  • Implemented a simple induction scheme for automatic feature learning and complexity determination.

    • Main Results:

    • The proposed model successfully learns higher-order label structures.
    • Applied the model to information extraction and image labeling tasks, demonstrating competitive performance.
    • The feature induction scheme automatically adapted model complexity to the data set.

    Conclusions:

    • The presented model offers an effective solution for discriminative labeling of structured data with complex label dependencies.
    • The approach mitigates the problem of model complexity explosion.
    • The model shows promise for real-world applications in information extraction and image labeling.