CaMEL: Case Marker Extraction Without Labels
MCML Authors
Leonie Weissweiler
* Former Member
Valentin Hofmann
Dr.
* Former Member
Masoud Jalili Sabet
Dr.
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
Abstract
Leonie Weissweiler
* Former Member
Valentin Hofmann
Dr.
* Former Member
Masoud Jalili Sabet
Dr.
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
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.
inproceedings
ACL 2022
60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland, May 22-27, 2022.
Authors
L. Weissweiler • V. Hofmann • M. J. Sabet • H. SchützeLinks
DOIResearch Area
BibTeXKey: WHJ+22