Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation

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This article presents a probabilistic hierarchical clustering model for morphological segmentation In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.
Original languageEnglish
Pages (from-to)349-374
Number of pages26
JournalComputational linguistics
Issue number2
Early online date4 Apr 2018
Publication statusE-pub ahead of print - 4 Apr 2018

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© 2018 Association for Computational Linguistics

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