document: Generative models for name disambiguation Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people . In this paper , we present an efficient framework by using two novel topic-based models , extended from Probabilistic Latent Semantic Analysis ( PLSA ) and Latent Dirichlet Allocation ( LDA ) . Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words . Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering . Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset .
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