Attribute Extraction

Summary

Attribute Extraction is a problem in the field of information extraction that focuses on identifying properties/features that describe a named entity. Performing attribute extract is often used in disambiguating person names, extracting encylopedic knowledge, and in improving question answering.

Common Approaches

Some approaches to Attribute Extraction include:

• Template/Pattern-Learning
  • Learn template contextual patterns using seed-based bootstrapping, and assign probability of attribute based on surrounding context.
• Extract then Verify
  • Two step procedure: First system uses rules, NER, gazetteer based matching, and patterns (manually created or learned) to extract all attribute candidates
  • Then verify candidates using a classifier (with features based on the context, pattern values, and dependency path) to trained determine if attribute value is correct for the given individual or should be discarded
• 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.

Evaluation

One venue of evaluation for the attribute extraction task has been the Web People Search workshop (WePS: Searching information about entities in the web), which has had a attribute extraction challenge in its past two workshops: WePS-2 Attribute Extraction Subtask Guidelines, WePS-3 Attribute Extraction Subtask Guidelines