category as “house parts” and point “quickly” as the intruder.

CNNSE (Compositional NNSE) is a variation of NNSE intended to allow word and phrase vector to adapt to the notion of composition by learning a DSM that supports semantic composition operations [@fyshe2015compositional]. CNNSE phrasal vector representations have shown to be a better match to actual phrase meanings when judged by human evaluators. JNNSE (Joint NNSE) is another NNSE variation that combines the representations obtained from large text corpora with brain activation data recorded while people read words [@fyshe2014interpretable].

The Explainable Principal Component Analysis (EPCA) [@brinton17framework] is yet another technique for generating interpretable vector representations. It builds upon the Principal Component Analysis (PCA) dimensionality reduction approach [@jolliffe1986principal], including a human-in-the-loop stage for refining the data. The EPCA process is performed iteratively: basis vectors generated by the regular PCA are analyzed by a (human) model designer, who excludes any word not related to the general category implied by all the other words in the vector, creating the first explainable basis vector. This vector is then excluded from the input data, over which regular PCA followed by the manual procedure are performed again, generating the second explainable basis vector. The process goes on 