Supervised Morphological Segmentation in a Low-Resource Learning Setting using Conditional Random Fields

Towards a First Automatic Unsupervised Morphological Segmentation for Inuinnaqtun

Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This resear…

Canonical and Surface Morphological Segmentation for Nguni Languages

Morphological Segmentation involves decomposing words into morphemes, the smallest meaning-bearing units of language. This is an important NLP task for morphologically-rich agglutinative languages such as the Southern Af…

MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation

Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the i…

Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields

Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages

Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class {``}squish{''}. In addition, many of these polysynthetic languages are low-resource. We propos…