Analogies in complex verb meaning shifts: The effect of affect in semantic similarity models

Abstract

We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to previous corpus-based studies, a novel dataset demonstrates that "regular" shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators.