Term Weighting Schemes for Latent Dirichlet Allocation

Many implementations of Latent Dirichlet Allocation (LDA), including those described in Blei et al. (2003), rely at some point on the removal of stopwords, words which are assumed to contribute little to the meaning of the text. This step is considered necessary because otherwise high-frequency words tend to end up scattered across many of the latent topics without much rhyme or reason. We show, however, that the ‘problem’ of high-frequency words can be dealt with more elegantly, and in a way that to our knowledge has not been considered in LDA, through the use of appropriate weighting schemes comparable to those sometimes used in Latent Semantic Indexing (LSI). Our proposed weighting methods not only make theoretical sense, but can also be shown to improve precision significantly on a non-trivial cross-language retrieval task.