Benjamin Schmidt (Northeastern University) has shared two posts about word embedding models–“Vector Space Models for the digital humanities” and “Rejecting the gender binary“–on his blog. Each post explores facets algorithms that can be used in digital humanities research. The first post provides an overview to word embedding models, contrasting them with topic models:
DHers use topic models because it seems at least possible that each individual topic can offer a useful operationalization of some basic and real element of humanities vocabulary: topics (Blei), themes (Jockers), or discourses (Underwood/Rhody).1 The word embedding models offer something slightly more abstract, but equally compelling: a spatial analogy to relationships between words.
The second post is a “more substantive look at how the method can help us better imagine a version of English without gendered language through some tricks of linear algebra.”