In recent years, the Digital Humanities have witnessed the steadily growing popularity of models from distributional semantics which can be used to model the meaning of documents and words in large digital text collections. Well-known examples of influential distributional models include Latent Dirichlet Allocation for topic modelling (Blei et al.) or Word2vec for estimating word vectors (Mikolov et al. 2013). Such distributional models have recently gained much prominence in the fields of Natural Language Processing and, more recently, Deep Representation Learning (Manning 2016). Humanities data is typically sparse and distributional models help scholars obtain smoother estimations of them. Whereas, for instance, words are conventionally encoded as binary ‘one-hot vectors’ in digital text analysis, embedding techniques from distributional semantics allow scholars to obtain dense, yet rich representations of vocabularies. These embedded representations are known to capture all sorts of valuable relationships between data points, although embedding techniques are typically trained using unsupervised objectives and require relatively little parameter tuning from scholars. Inspiring applications of this emergent technology in DH have ranged from more technical work in cultural studies at large (Bamman et al. 2014), case studies in literary history (Mimno 2012; Schoech 2017) or valuable DH-oriented web apps, such as ShiCo (Martinez-Ortiz et al. 2016). The availability of high-quality implementations in the public domain, in software suites as gensim, word2vec, or mallet etc. has greatly added these methods’ popularity.