Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Unsupervised learning of the morphology of a natural language
Computational Linguistics
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Unsupervised models for morpheme segmentation and morphology learning
ACM Transactions on Speech and Language Processing (TSLP)
Simple Morpheme Labelling in Unsupervised Morpheme Analysis
Advances in Multilingual and Multimodal Information Retrieval
Overview of Morpho challenge 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Allomorfessor: towards unsupervised morpheme analysis
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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
Applying morphological decomposition to statistical machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Semi-supervised learning of concatenative morphology
SIGMORPHON '10 Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology
Morpho Challenge competition 2005--2010: evaluations and results
SIGMORPHON '10 Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology
Probabilistic hierarchical clustering of morphological paradigms
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Allomorfessor extends the unsupervised morpheme segmentation method Morfessor to account for the linguistic phenomenon of allomorphy, where one morpheme has several different surface forms. The method discovers common base forms for allomorphs from an unannotated corpus by finding small modifications, called mutations, for them. Using Maximum a Posteriori estimation, the model is able to decide the amount and types of the mutations needed for the particular language. In Morpho Challenge 2009 evaluations, the effect of the mutations was discovered to be rather small. However, Allomorfessor performed generally well, achieving the best results for English in the linguistic evaluation, and being in the top three in the application evaluations for all languages.