Unsupervised learning of the morphology of a natural language
Computational Linguistics
Unsupervised learning of morphology using a novel directed search algorithm: taking the first step
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Unsupervised discovery of morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Learning probabilistic paradigms for morphology in a latent class model
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Overview and results of Morpho challenge 2009
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
MorphoNet: exploring the use of community structure for unsupervised morpheme analysis
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Unsupervised morpheme analysis with allomorfessor
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Clustering morphological paradigms using syntactic categories
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Discovering morphological paradigms from plain text using a Dirichlet process mixture model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-of-art systems.