Paper: Fine-Grained Classification of Named Entities Exploiting Latent Semantic Kernels

ACL ID W09-1125
Title Fine-Grained Classification of Named Entities Exploiting Latent Semantic Kernels
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009
Authors

We present a kernel-based approach for fine- grained classification of named entities. The only training data for our algorithm is a few manually annotated entities for each class. We defined kernel functions that implicitly map entities, represented by aggregating all con- texts in which they occur, into a latent seman- tic space derived from Wikipedia. Our method achieves a significant improvement over the state of the art for the task of populating an ontology of people, although requiring con- siderably less training instances than previous approaches.