Attribute Extraction

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Summary

Attribute Extraction is a problem in the field of information extraction that focuses on identifying properties/features that describe a named entity.

Common Approaches

Some approaches to Attribute Extraction include:

  • Template/Pattern-Learning: Learn template contextual patterns using seed-based bootstrapping
  • Position Based: Basing predictions on absolute and relative ordering of where the attribute values typically appear in documents.
  • Transitivity-Based: Using transitivity of attributes across co-occuring entities. Co-occuring entities, such as people mentioned in a given person's biography page, tend to have similar attributes.
  • Latent-Based: Detect attributes that may not directly be mentioned in an article based on a topic-model.

Challenges / Issues

Some challenges in Attribute Extraction include ...

References / Links

  • Nikesh Garera and David Yarowsky. Structural, Transitive and Latent Models for Biographic Fact Extraction. - [1]

Relevant Papers