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%.
|Publication status||Published - 2020|
|Event||58th Annual Meeting of the Association for Computational Linguistics, ACL2020 - Online|
Duration: 1 Jan 2020 → …
|Conference||58th Annual Meeting of the Association for Computational Linguistics, ACL2020|
|Period||1/01/20 → …|