CaMEL: Case Marker Extraction without Labels

Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet, Hinrich Schuetze


Abstract
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we automatically construct a silver standard from UniMorph. The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.
Anthology ID:
2022.acl-long.377
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5506–5516
Language:
URL:
https://aclanthology.org/2022.acl-long.377
DOI:
10.18653/v1/2022.acl-long.377
Bibkey:
Cite (ACL):
Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet, and Hinrich Schuetze. 2022. CaMEL: Case Marker Extraction without Labels. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5506–5516, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
CaMEL: Case Marker Extraction without Labels (Weissweiler et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.377.pdf
Software:
 2022.acl-long.377.software.zip
Video:
 https://aclanthology.org/2022.acl-long.377.mp4
Code
 leonieweissweiler/camel