TY - GEN
T1 - To compress or not to compress? A finite-state approach to nen verbal morphology
AU - Muradoglu, Saliha
AU - Evans, Nicholas
AU - Suominen, Hanna
N1 - Publisher Copyright:
© 2020 Association for Computational Linguistics.
PY - 2020
Y1 - 2020
N2 - 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 decomposed 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%.
AB - 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 decomposed 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%.
UR - http://www.scopus.com/inward/record.url?scp=85099177391&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 207
EP - 213
BT - ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Student Research Workshop, SRW 2020
Y2 - 5 July 2020 through 10 July 2020
ER -