Leveraging computational methods for morphological description: a case study of Nen
Creators: name: Muradoglu, Saliha
Publisher: name: The Australian National University
Year: 2024

description: Corpus building is a resource-hungry venture. Collecting, analysing, and maintaining are essential stages of corpus building. Modern technology has allowed for collection to be more accessible and, to an extent, has eased the analysis cost. Nevertheless, with the increasing rate at which data can be captured, the resource demands for analysis still need to be lowered. Language documentation poses an acute instantiation of this problem. The extremely limited language descriptions (if any) that we have of most of the world's 7000+ languages, and the needs for community access, only add to the difficulty of sufficiently capturing a language. This interdisciplinary thesis aims to catalogue the process of enlisting the aid of computational methods for morphological description. The Nen language is used as a motivating case study. Nen is a member of the Morehead-Yam language family of Southern New Guinea. It is an under-resourced language that is actively being described and documented. It provides an interesting case study as it exhibits distributed exponence - a non-trivial means of mapping form to meaning. Its immense morphological complexity and ongoing documentation status make it an excellent playground for providing a real-time account of the process. In an almost pedagogical order, the thesis presents the development of computational resources for Nen and describes issues, caveats, and opportunities for applying existing computational methods to language documentation. The thesis employs two infrastructures for morphological analysis, finite-state transducers and supervised machine learning systems. The thesis presents the first morphological analyser of Nen and explores the difference between linguist and computational needs. It finds that where a linguist might consider a principal parts approach optimal for description, computational performance is impartial to a principal part or `chunking' approach. The resultant architecture is different, but the accuracies achieved are the same. The thesis extends its morphological analysis of Nen with state-of-the-art neural architecture. Its results show no significant difference between random and Zipfian sampling methods, and minor differences may be attributed to the training set composition differences. It introduces empirical evidence highlighting training data composition's effects on model performance. The errors generated by each system are extensively analysed. The most common error types are found to be allomorphy errors, misapplication of morphophonological rules or feature category mappings. Furthermore, the thesis findings indicate that the model learns paradigmatic information as well as string transduction. It predicts syncretism in an unseen form where the rest of the paradigm exhibits syncretism. The thesis explores the concept of paradigm coverage, a consideration born from the needs of language documentation. The question, 'how much data is enough to capture a language' has haunted documentary linguistics for decades. The thesis presents another way to measure completeness by leveraging computational models as another line of inquiry; given the existing corpus, how much of the paradigm can a deep learning model capture correctly? It distinguishes between attestation and heuristic coverage; the former describes the corpus presence of specific lemmata and inflectional features, while the latter refers to the discovery process of a morphological paradigm. Finally, the thesis proposes using active learning to minimise the cost of annotation by prioritising examples where the model showcases difficulty. To explore the viability of this strategy, it extends its subject of study to 30 linguistically diverse languages. It finds that data selection based on model confidence improves model performance more rapidly than random selection. Results that these metrics are robust to language typology, with the same behaviour observed across 30 languages.