Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning Morphology - Practice Makes Good
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Learning Multilingual Morphology with CLOG
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Computational Complexity of Problems on Probabilistic Grammars and Transducers
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Memory-based morphological analysis
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Using induced rules as complex features in memory-based language learning
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Ripple Down Rule learning for automated word lemmatisation
AI Communications
A global model for joint lemmatization and part-of-speech prediction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Bayesian inference for finite-state transducers
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Research on Language and Computation
Using learned conditional distributions as edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Large scale inference of deterministic transductions: tenjinno problem 1
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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This paper discusses the supervised learning of morphology using stochastic transducers, trained using the Expectation-Maximization (EM) algorithm. Two approaches are presented: first, using the transducers directly to model the process, and secondly using them to define a similarity measure, related to the Fisher kernel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic.