Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation

TY - JOUR

T1 - Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation

AU - Manandhar, Suresh Kumar

AU - Can, Burcu

N1 - © 2018 Association for Computational Linguistics

PY - 2018/4/4

Y1 - 2018/4/4

N2 - 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.

AB - 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.

U2 - 10.1162/COLI_a_00318

DO - 10.1162/COLI_a_00318

M3 - Article

SN - 0891-2017

VL - 44

SP - 349

EP - 374

JO - Computational linguistics

JF - Computational linguistics

IS - 2

ER -