North S\'ami morphological segmentation with low-resource semi-supervised sequence labeling

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…

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…

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation

Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries …

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…