In the face of the recent rise of distributed neural representations as a means of capturing lexical meaning in NLP tasks (Collobert et al., 2011;Mikolov et al., 2013a;Pennington et al., 2014), our goal in this paper is to model attribute meaning based on word embeddings.
we use CBOW embeddings of adjectives and nouns (Mikolov et al., 2013a) as underlying word representations and train a compositionality function in order to compute a phrase representation that is predictive of the implicitly conveyed attribute meaning.