Manandhar, Suresh Kumar orcid.org/0000-0002-2822-2903 and Can, Burcu (2018) Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation. Computational linguistics. pp. 349-374. ISSN 0891-2017
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Association for Computational Linguistics |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 15 Jun 2018 07:50 |
Last Modified: | 21 Nov 2024 00:36 |
Published Version: | https://doi.org/10.1162/COLI_a_00318 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1162/COLI_a_00318 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132127 |
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Description: Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
Licence: CC-BY-NC-ND 2.5