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Issue Date
2014-12-31
Metadata
Show full item recordAbstract
This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Citation
Can B., Manandhar S. (2014) Methods and Algorithms for Unsupervised Learning of Morphology. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54906-9_15Publisher
SpringerAdditional Links
https://link.springer.com/chapter/10.1007/978-3-642-54906-9_15Type
Conference contributionLanguage
enDescription
This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15 The accepted version of the publication may differ from the final published version.Series/Report no.
Lecture Notes in Computer Science, vol 8403ISSN
0302-9743EISSN
1611-3349ISBN
9783642549069ae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-54906-9_15
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/