Distinguishing between paradigmatic semantic relations across word classes: human ratings and distributional similarity

Abstract

This article explores the distinction between paradigmatic semantic relations, both from a cognitive and a computational linguistic perspective. Focusing on an existing dataset of German synonyms, antonyms and hypernyms across the word classes of nouns, verbs and adjectives, we assess human ratings and a supervised classification model using window-based and pattern-based distributional vector spaces. Both perspectives suggest differences in relation distinction across word classes, but easy vs. difficult class–relation combinations differ, exhibiting stronger ties between ease and naturalness of classdependent relations for humans than for computational models. In addition, we demonstrate that distributional information is indeed a difficult starting point for distinguishing between paradigmatic relations but that even a simple classification model is able to manage this task. The fact that the most salient vector spaces and their success vary across word classes and paradigmatic relations suggests that combining feature types for relation distinction is better than applying them in isolation.