Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
Inference of variable-length linguistic and acoustic units by multigrams
Speech Communication
Self-Supervised Chinese Word Segmentation
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Unsupervised language acquisition
Unsupervised language acquisition
Modeling and learning multilingual inflectional morphology in a minimally supervised framework
Modeling and learning multilingual inflectional morphology in a minimally supervised framework
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
Knowledge-free induction of inflectional morphologies
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Unsupervised discovery of morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Morphemes as necessary concept for structures discovery from untagged corpora
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Induction of a simple morphology for highly-inflecting languages
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
ParaMor and Morpho challenge 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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Allomorphic variation, or form variation among morphs with the same meaning, is a stumbling block to morphological induction (MI). To address this problem, we present a hybrid approach that uses a small amount of linguistic knowledge in the form of orthographic rewrite rules to help refine an existing MI-produced segmentation. Using rules, we derive underlying analyses of morphs---generalized with respect to contextual spelling differences---from an existing surface morph segmentation, and from these we learn a morpheme-level segmentation. To learn morphemes, we have extended the Morfessor segmentation algorithm [Creutz and Lagus 2004; 2005; 2006] by using rules to infer possible underlying analyses from surface segmentations. A segmentation produced by Morfessor Categories-MAP Software v. 0.9.2 is used as input to our procedure and as a baseline that we evaluate against. To suggest analyses for our procedure, a set of language-specific orthographic rules is needed. Our procedure has yielded promising improvements for English and Turkish over the baseline approach when tested on the Morpho Challenge 2005 and 2007 style evaluations. On the Morpho Challenge 2007 test evaluation, we report gains over the current best unsupervised contestant for Turkish, where our technique shows a 2.5% absolute F-score improvement.