We examine the proposed priors using three commonly sized English corpora for textual analysis within CSSDH: the top 100 list of books in Project Gutenberg (2019), a sample from Twitter (Go et al., 2009) and the U.S. Congress Speeches 1981-2016 (Gentzkow et al., 2018).
consequently, the dot product for these vectors will be 0 for all dimensions except K, and thus the effect of the anchored word types on the rest of the vocabulary will exclusively depend on K. this implies that, as γ, ω → 0, word types within V − and V + obtain exactly the same word embedding.