disambiguation of medline abstracts using topic models
Topic models are an established technique for generating information about the subjects discussed in collections of documents. Latent Dirichlet Allocation (LDA) is a widely applied topic model. The topic models generated by LDA consist of sets of terms associated with each topic and these are used to provide context for a Word Sense Disambiguation (WSD) system. It is found that using this context leads to a statistically significant improvement in the performance of a graph-based WSD system when applied to a standard evaluation resource.
topic models latent dirichlet allocation lda word sense disambiguation wsd natural language processing personalized pagerank nlp