A Topic Model for Word Sense Disambiguation

We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider each word. Using the WORDNET hierarchy, we embed the construction of Abney and Light (1999) in the topic model and show that automatically learned domains improve WSD accuracy compared to alternative contexts.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu
• Keywords: EMNLP 2007, Natural Language Processing, Probabilistic Posterior Inference, Topic Model, Unsupervised Probabilistic Topic