Abstract
We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to 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.- Anthology ID:
- N18-2024
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 150–156
- Language:
- URL:
- https://aclanthology.org/N18-2024
- DOI:
- 10.18653/v1/N18-2024
- Cite (ACL):
- Maximilian Köper and Sabine Schulte im Walde. 2018. Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 150–156, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models (Köper & Schulte im Walde, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-2024.pdf