We describe the University of Maryland's submission to SemEval-018 Task 10, "Capturing Discriminative Attributes": given word triples (w 1 , w 2 , d), the goal is to determine whether d is a discriminating attribute belonging to w 1 but not w 2 . Our study aims to determine whether word embeddings can address this challenging task. Our submission casts this problem as supervised binary classification using only word embedding features. Using a gaussian SVM model trained only on validation data results in an F-score of 60%. We also show that cosine similarity features are more effective, both in unsupervised systems (F-score of 65%) and supervised systems (F-score of 67%).