In spite of their huge potential for Digital Humanities, multiple aspects of their application still remain untapped. Unsupervised models such as Word2vec, for instance, are notoriously hard to evaluate directly – often researchers have to resort to indirect evaluations in this respect. This renders it intriguing to which extent the output of distributional models should play a decisive role in hermeneutical debates or controversies in the Humanities. With other techniques for Distant Reading, distributional models moreover share the drawback that they typically only yield a <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">single reading </hi> for a particular corpus so that for example the polysemy of a word isn’t rendered adequately. Interesting progress into representing the complex variability of meaning has been achieved, for example on the level of diachronic word embeddings, where convincing attempts have been made to allow for semantic shifts in an individual word’s meaning (Hamilton et al. 2016). Likewise, critical studies have revealed how tightly distributional models reproduce cultural biases with respect to gender and race (Bolukbasi et al. 2016), which calls for a debate about the ethical aspects of the matter. Likewise, it deserves emphasis how distributional models depend on large datasets and typically yield poor estimates for more restrictive data collections. This might help explain why word embeddings so far have not that many applications in fields like stylometry, that mostly work with relatively small corpora.