Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Unsupervised learning of the morphology of a natural language
Computational Linguistics
An algorithm for the unsupervised learning of morphology
Natural Language Engineering
Improving statistical MT through morphological analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Priors in Bayesian learning of phonological rules
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
Unsupervised induction of natural language morphology inflection classes
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
Morphology induction from term clusters
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Research on Language and Computation
Discovering morphological paradigms from plain text using a Dirichlet process mixture model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The study of effect of length in morphological segmentation of agglutinative languages
MM '12 Proceedings of the First Workshop on Multilingual Modeling
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Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is insufficient (e.g., taking may be more appropriately analyzed as take+ing, with a spelling rule accounting for the deletion of the stem-final e). In this paper, we develop a Bayesian model for simultaneously inducing both morphology and spelling rules. We show that the addition of spelling rules improves performance over the baseline morphology-only model.