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Title of the paper |
Entity Resolution in Texts Using Statistical Learning and Ontologies |
Author(s) |
Tadej Štajner, Dunja Mladenić |
Location |
Shanghai, China |
Date |
6 - 9. December 2009 |
Link to Eventpage |
|
Abstract |
Ambiguities, which are inherently present in natural languages represent a challenge of determining the actual identities of entities mentioned in a document (e.g., Pariscan refer to a city in France but it can also refer to a small city in Texas, USA or to a 1984 film directed by Wim Wenders having title Paris, Texas). Disambiguation is a problem that can be successfully solved by entity resolution methods. This paper studies various methods for estimating relatedness between entities, used in collective entity resolution. We define a unified entity resolution approach, capable of using implicit as well as explicit relatedness for collectively identifying in-text entities. As a relatedness measure, we propose a method, which expresses relatedness using the heterogeneous relations of a domain ontology. We also experiment with other relatedness measures, such as using statistical learning of co-occurrences of two entities or using content similarity between them. Evaluation on real data shows that the new methods for relatedness estimation give good results. |
Last updated: |
10-02-2010 18:26 |
Created: |
08-02-2010 15:56 |