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Methods and algorithms for unsupervised learning of morphology
Can, Burcu Manandhar, Suresh
Can, Burcu
Manandhar, Suresh
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2014-12-31
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Abstract
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.
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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_15
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Conference contribution
Language
en
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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
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Series/Report no.
Lecture Notes in Computer Science, vol 8403
ISSN
0302-9743
EISSN
1611-3349
ISBN
9783642549069