SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction

Anna Hätty, Dominik Schlechtweg, Sabine Schulte im Walde


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
Editors:
Rada Mihalcea, Ekaterina Shutova, Lun-Wei Ku, Kilian Evang, Soujanya Poria
Venue:
*SEM
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://aclanthology.org/S19-1001
DOI:
10.18653/v1/S19-1001
Bibkey:
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., *SEM 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-1001.pdf