Word vector averaging has moreover proven surprisingly effective for learning relation vectors (Weston et al., 2013;Hashimoto et al., 2015;Fan et al., 2015; Espinosa Anke and Schockaert, 2018), as well as in related tasks such as sentence embedding (Wieting et al., 2016).
as is customary in the literature, we compute Pearson correlation with respect to these predefined semantic properties, and measure how well a given set of word vectors is able to predict them, with higher being better.