vector summarizing their patterns of co-occurrence in large text corpora. The vector representations usually go through a dimensionality reduction process, a mathematical operation that makes the vectors more manageable while still capturing the co-occurrence patterns [@baroni2010strudel], being the most common technique the Singular Value Decompositions (SVD) [@klema1980singular].

Dimensionality reduction results in vectors whose features correspond to very broad domains of knowledge, such as “food”, “sports” or “education”, for example. A direct consequence of this new representation, as observed by Baroni et al. [-@baroni2010strudel], is that the underlying abstraction behind most DSMs, the Vector Space Models (VSMs), “might be very good at finding out that two concepts are similar, but they tell us little about the internal structure of concepts and, hence, why or how they are similar” [@baroni2010strudel]. What can be obtained is an overall similarity score that does not convey any additional information about the relationship between similar words. SVD, in particular, produces matrices where, for most dimensions, it is hard to interpret what a high or low score entails for the semantics of a given word [@fyshe2015compositional]. This is illustrated in the example posed by Murphy et al. [-@murphy2012learning], where they retrieve the latent dimension of an SVD-based 