Abstract
We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts. We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors. Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.- Anthology ID:
- S19-1001
- Volume:
- Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–8
- Language:
- URL:
- https://aclanthology.org/S19-1001
- DOI:
- 10.18653/v1/S19-1001
- Cite (ACL):
- Anna Hätty, Dominik Schlechtweg, and Sabine Schulte im Walde. 2019. SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 1–8, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction (Hätty et al., SemEval-*SEM 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-1001.pdf