Comparing Ref-Vectors and word embeddings in a verb semantic similarity task

This work introduces reference vectors (Ref-Vectors), a new kind of word vectors in which the semantics is determined by the property of words to refer to world entities (i.e. objects or events), rather than by contextual information retrieved in a corpus. Ref-Vectors are here compared with state-of-the-art word embeddings in a verb semantic similarity task. The SimVerb-3500 dataset has been used as a benchmark to verify the presence of a statistical correlation between the semantic similarity derived by human judgments and those measured with Ref-Vectors and verb embeddings. Results show that Ref-Vector similarities are closer to human judgments, proving that, within the action domain, these vectors capture verb semantics better than word embeddings.