Abstract
This paper describes the development of a verbal morphological parser for an under-resourced Papuan language, Nen. Nen verbal morphology is particularly complex, with a transitive verb taking up to 1,740 unique features. The structural properties exhibited by Nen verbs raises interesting choices for analysis. Here we compare two possible methods of analysis: ‘Chunking’ and decomposition. ‘Chunking’ refers to the concept of collating morphological segments into one, whereas the decomposition model follows a more classical linguistic approach. Both models are built using the Finite-State Transducer toolkit foma. The resultant architecture shows differences in size and structural clarity. While the ‘Chunking’ model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order. In this paper, we describe the challenges encountered when modelling a language exhibiting distributed exponence and present the first morphological analyser for Nen, with an overall accuracy of 80.3%.- Anthology ID:
- 2020.acl-srw.28
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Shruti Rijhwani, Jiangming Liu, Yizhong Wang, Rotem Dror
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 207–213
- Language:
- URL:
- https://aclanthology.org/2020.acl-srw.28
- DOI:
- 10.18653/v1/2020.acl-srw.28
- Cite (ACL):
- Saliha Muradoglu, Nicholas Evans, and Hanna Suominen. 2020. To compress or not to compress? A Finite-State approach to Nen verbal morphology. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 207–213, Online. Association for Computational Linguistics.
- Cite (Informal):
- To compress or not to compress? A Finite-State approach to Nen verbal morphology (Muradoglu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.acl-srw.28.pdf