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
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
Using unsupervised paradigm acquisition for prefixes
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Semi-supervised learning of concatenative morphology
SIGMORPHON '10 Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology
Unsupervised morpheme analysis with allomorfessor
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Unsupervised morphological analysis by formal analogy
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Unsupervised word decomposition with the promodes algorithm
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Simulating morphological analyzers with stochastic taggers for confidence estimation
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
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This paper describes a system for unsupervised morpheme analysis and the results it obtained at Morpho Challenge 2007. The system takes a plain list of words as input and returns a list of labelled morphemic segments for each word. Morphemic segments are obtained by an unsupervised learning process which can directly be applied to different natural languages. Results obtained at competition 1 (evaluation of the morpheme analyses) are better in English, Finnish and German than in Turkish. For information retrieval (competition 2), the best results are obtained when indexing is performed using Okapi (BM25) weighting for all morphemes minus those belonging to an automatic stop list made of the most common morphemes.