Unsupervised Morphology Learning with Statistical Paradigms

COLING 2018 Hongzhi XuMitchell MarcusCharles YangLyle Ungar

This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them... (read more)

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