Difference between revisions of "Attribute Extraction"
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Some approaches to Attribute Extraction include: | Some approaches to Attribute Extraction include: | ||
| − | * '''Template/Pattern-Learning''': Learn template contextual patterns using seed-based bootstrapping | + | * '''Template/Pattern-Learning''': Learn template contextual patterns using seed-based bootstrapping. Variations of this method are generally the most used approaches found in literature. |
| + | * '''Rule-based''': | ||
* '''Position Based''': Basing predictions on absolute and relative ordering of where the attribute values typically appear in documents. | * '''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. | * '''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. | * '''Latent-Based''': Detect attributes that may not directly be mentioned in an article based on a topic-model. | ||
| − | * | + | * |
== Evaluation == | == Evaluation == | ||
Revision as of 20:04, 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. 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. Variations of this method are generally the most used approaches found in literature.
- Rule-based:
- 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
