  Similarity is a core notion that is used in psychology and two branches of
linguistics: theoretical and computational. The similarity datasets that come
from the two fields differ in design: psychological datasets are focused around
a certain topic such as fruit names, while linguistic datasets contain words
from various categories. The later makes humans assign low similarity scores to
the words that have nothing in common and to the words that have contrast in
meaning, making similarity scores ambiguous. In this work we discuss the
similarity collection procedure for a multi-category dataset that avoids score
ambiguity and suggest changes to the evaluation procedure to reflect the
insights of psychological literature for word, phrase and sentence similarity.
We suggest to ask humans to provide a list of commonalities and differences
instead of numerical similarity scores and employ the structure of human
judgements beyond pairwise similarity for model evaluation. We believe that the
proposed approach will give rise to datasets that test meaning representation
models more thoroughly with respect to the human treatment of similarity.
