The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Inducing probabilistic grammars by Bayesian model merging
Open accessAbstract
We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are incorporated by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are merged to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (‘Occam's Razor’). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based n-grams, and stochastic context-free grammars.
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Cites
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Cited by
Learning Accurate, Compact, and Interpretable Tree Annotation
Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220230 · Cited by 793
Identifying Hierarchical Structure in Sequences: A linear-time algorithm
Nevill-Manning, C. G., & Witten, I. H. (1997) · Journal of Artificial Intelligence Research · doi:10.1613/jair.374 · Cited by 549
Distributional regularity and phonotactic constraints are useful for segmentation
Brent, M. R., & Cartwright, T. A. (1996) · Cognition · doi:10.1016/s0010-0277(96)00719-6 · Cited by 514
“MRL has been applied to language (e.g., Ellison, in press; Stolcke & Omohundro, 1994) and to modeling language acquisition (Brent, Gafos, & Cartwright, 1994).”
MethodCorpus-based induction of syntactic structure: models of dependency and constituency
Klein, D., & Manning, C. D. (2004) · Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04 · doi:10.3115/1218955.1219016 · Cited by 440
Unsupervised learning of natural languages
Solan, Z., Horn, D., Ruppin, E., & Edelman, S. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409746102 · Cited by 346
Metadata
- Authors
- Stolcke, A., & Omohundro, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1994
- Pages
- 106-118
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1575798196
- Cited by
- 230
- References
- 3
- FWCI
- 7.08
- Type
- conference paper
- Language
- en
- Open access
- green
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