Yang Song
ABSTRACT
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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Digital Library
- T. Hofmann. Probabilistic Latent Semantic Indexing. In SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 5057, Berkeley, California. Google ScholarDigital Library
Index Terms
- Generative models for name disambiguation
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