This article describes the unsupervised strategy submitted by the CitiusNLP team to SemEval 2018 Task 10, a task which consists of predicting whether a word is a discriminative attribute between two other words. The proposed strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70% accuracy when it is applied on the development dataset, but its accuracy goes down (63%) on the official test dataset.
Keywords: distributional semantics, dependency analysis, discriminative attributes, similarity