Property inference involves predicting properties for a word from its distributional representation. We focus on human-generated resources that link words to their properties and on the task of predicting these properties for unseen words. We introduce the use of label propagation, a semi-supervised machine learning approach, for this task and, in the first systematic study of models for this task, find that label propagation achieves state-of-the-art results. For more variety in the kinds of properties tested, we introduce two new property datasets.
