Unsupervised learning of the morphology of a natural language
Computational Linguistics
A Bayesian model for morpheme and paradigm identification
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Unsupervised segmentation of words using prior distributions of morph length and frequency
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Contextual dependencies in unsupervised word segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Morphological analysis for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Unsupervised morphological segmentation with log-linear models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Improving morphology induction by learning spelling rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Morphological Analyzer for Agglutinative Languages Using Machine Learning Approaches
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Morph length is one of the indicative feature that helps learning the morphology of languages, in particular agglutinative languages. In this paper, we introduce a simple unsupervised model for morphological segmentation and study how the knowledge of morph length affect the performance of the segmentation task under the Bayesian framework. The model is based on (Goldwater et al., 2006) unigram word segmentation model and assumes a simple prior distribution over morph length. We experiment this model on two highly related and agglutinative languages namely Tamil and Telugu, and compare our results with the state of the art Morfessor system. We show that, knowledge of morph length has a positive impact and provides competitive results in terms of overall performance.