We present a kernel-based approach for finegrained 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.
The approach presented in REF classifies people on an excerpt of the WordNet ontology, using kernel functions that implicitly map entities, represented by aggregating all contexts in which they occur, into a latent semantic space derived from Wikipedia.
Fine-Grained Classification of Named Entities Exploiting Latent Semantic Kernels
CoNLL
Computer Science