Structured generative models for unsupervised named-entity clustering
Micha Elsner, Eugene Charniak, Mark S. Johnson · 2009
We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun resolver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsupervised model, and the best score for a generative model.