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 even share 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. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Search and Retrieval General Terms Algorithms, Experimentation, Theory Keywords Unsupervised Machine Learning, Name Disambiguation.

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Yang Song, Jian Huang 0002, Isaac G. Councill, Jia Li, C. Lee Giles
• Keywords: Internet Technology, Keywords Unsupervised Machine, Latent Dirichlet Allocation, Latent Semantic Analysis, WWW 2007