Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
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 unsu...
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2018-06-01
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doaj-175f8b45e5854742b8f1694bdd62f7202020-11-25T01:45:01ZengThe MIT PressComputational Linguistics1530-93122018-06-0144234937410.1162/coli_a_00318coli_a_00318Tree Structured Dirichlet Processes for Hierarchical Morphological SegmentationBurcu Can0Suresh Manandhar1Hacettepe University, Department of Computer Engineering. burcucan@cs.hacettepe.edu.trUniversity of York, Department of Computer Science. suresh@cs.york.ac.ukThis 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.https://www.mitpressjournals.org/doi/pdf/10.1162/coli_a_00318 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Burcu Can Suresh Manandhar |
spellingShingle |
Burcu Can Suresh Manandhar Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation Computational Linguistics |
author_facet |
Burcu Can Suresh Manandhar |
author_sort |
Burcu Can |
title |
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation |
title_short |
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation |
title_full |
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation |
title_fullStr |
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation |
title_full_unstemmed |
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation |
title_sort |
tree structured dirichlet processes for hierarchical morphological segmentation |
publisher |
The MIT Press |
series |
Computational Linguistics |
issn |
1530-9312 |
publishDate |
2018-06-01 |
description |
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. |
url |
https://www.mitpressjournals.org/doi/pdf/10.1162/coli_a_00318 |
work_keys_str_mv |
AT burcucan treestructureddirichletprocessesforhierarchicalmorphologicalsegmentation AT sureshmanandhar treestructureddirichletprocessesforhierarchicalmorphologicalsegmentation |
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1725025775650865152 |