Difference between revisions of "Attribute Extraction"
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== Common Approaches == | == Common Approaches == | ||
| − | Some | + | 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 == | == Challenges / Issues == | ||
Revision as of 19:42, 30 November 2010
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]
