Fine-grained classification of named entities exploiting latent semantic kernels

Abstract

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 contexts in which they occur, into a latent semantic 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 considerably less training instances than previous approaches.