Neural language models, originating from the word2vec algorithm (Mikolov et al., 2013a; Mikolov et al., 2013b; Mikolov et al., 2013c) , are currently considered as state-of-the-art solutions for implementing this assumption (Schnabel et al., 2015) . Within this approach, changes in similarity relations between lexical items at two different points of time are interpreted as a signal for meaning shift. Accordingly, lexical items which are very similar to the lexical item under scrutiny can be considered as approximating its meaning at a given point in time. Both techniques were already combined in prior work to show, e.g., the increasing association of the lexical item "gay" with the meaning dimension of "homosexuality"<cite> (Kim et al., 2014</cite>; Kulkarni et al., 2015) .