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Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models

Maximilian Köper, Sabine Schulte im Walde


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N18-2024.pdf