The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Unsupervised Learning of the Morphology of a Natural Language
Open accessAbstract
This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
An Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitányi, P. (2019) · Texts in Computer Science · doi:10.1007/978-3-030-11298-1 · Cited by 3,681
Journal of the Royal Statistical Society (B)
Fildes, R. (1993) · International Journal of Forecasting · doi:10.1016/0169-2070(93)90088-5 · Cited by 3,437
Stochastic Complexity in Statistical Inquiry
Rissanen, J. (1998) · WORLD SCIENTIFIC eBooks · doi:10.1142/0822 · Cited by 1,539
The Logical Structure of Linguistic Theory
Chomsky, N. (1975) · MPG.PuRe (Max Planck Society) · Cited by 1,221
Autosegmental and Metrical Phonology
Rice, K., & Goldsmith, J. A. (1992) · Language · doi:10.2307/416372 · Cited by 1,057
Self-citationBell, T., Cleary, J. G., & Witten, I. H. (1990) · Cited by 988
Eugene Charniak, D. (1994) · Medical Entomology and Zoology · Cited by 811
Two-Level Morphology: A General Computational Model for Word-Form Recognition and Production
Koskenniemi, K. (1983) · Työväentutkimus Vuosikirja · Cited by 783
Simplicity: a unifying principle in cognitive science?
Chater, N., & Vitányi, P. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00005-0 · Cited by 494
Cited by
Simplicity: a unifying principle in cognitive science?
Chater, N., & Vitányi, P. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00005-0 · Cited by 494
Probabilistic models of language processing and acquisition
Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.006 · Cited by 411
Morph-based speech recognition and modeling of out-of-vocabulary words across languages
Creutz, M., Hirsimäki, T., Kurimo, M., Puurula, A., Pylkkönen, J., Siivola, V., Varjokallio, M., Arisoy, E., Saraçlar, M., & Stolcke, A. (2007) · ACM Transactions on Speech and Language Processing · doi:10.1145/1322391.1322394 · Cited by 127
Unlimited vocabulary speech recognition with morph language models applied to Finnish
Hirsimäki, T., Creutz, M., Siivola, V., Kurimo, M., Virpioja, S., & Pylkkönen, J. (2006) · Computer Speech & Language · doi:10.1016/j.csl.2005.07.002 · Cited by 124
(2007) · Cited by 75
Metadata
- Authors
- Goldsmith, J.
- Venue
- Computational Linguistics
- Publisher
- MIT Press
- Published
- 2001-06
- Volume
- 27
- Issue
- 2
- Pages
- 153-198
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2101711363
- Cited by
- 788
- References
- 20
- FWCI
- 30.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
