Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Latent-variable modeling of string transductions with finite-state methods
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Graphical models over multiple strings
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Grammatical Inference: Learning Automata and Grammars
Grammatical Inference: Learning Automata and Grammars
Estimating strictly piecewise distributions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
On languages piecewise testable in the strict sense
MOL'07/09 Proceedings of the 10th and 11th Biennial conference on The mathematics of language
Aural Pattern Recognition Experiments and the Subregular Hierarchy
Journal of Logic, Language and Information
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Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention to what is minimally necessary to describe and learn phonological feature interactions in phonotactic patterns. The "bottom-up" approach adopted here is contrasted with the "top-down" approach in Hayes and Wilson (2008), and it is argued that the bottom-up approach is more analytically transparent.