Structured generative models for unsupervised named-entity clustering

Abstract

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 re-solver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsuper-vised model, and the best score for a generative model. © 2009 Association for Computational Linguistics.