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.
CITATION STYLE
Köper, M., & Im Walde, S. S. (2018). Analogies in complex verb meaning shifts: The effect of affect in semantic similarity models. In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference (Vol. 2, pp. 150–156). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n18-2024
Mendeley helps you to discover research relevant for your work.