Tree structured Dirichlet processes for hierarchical morphological segmentation
AbstractThis 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.
CitationCan, B. and Manandhar, S. (2018) Tree structured Dirichlet processes for hierarchical morphological segmentation, Computational Linguistics, 44(2), pp. 349-374.
Description© 2018 The Authors. Published by MIT Press/ACL. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/coli_a_00318
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