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

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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.

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APA

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

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