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Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models
Mark Johnson, Thomas L. Griffiths, Sharon Goldwater, 2007. Advances in Neural Information Processing Systems 19.
Introduces adaptor grammars, probabilistic grammars with a Pitman-Yor process that lets them learn reusable units such as words and morphemes without supervision.
What it's used for here
Four surveys propose adaptor grammars for segmenting the manuscript's words into units.
Cited by on this site 4 records
4 methods surveys
SRCSignal processing, mathematics, linguistics, inductive methods
Cite it
Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. Advances in Neural Information Processing Systems 19. https://doi.org/10.7551/mitpress/7503.003.0085
@article{johnson2007adaptor,
author = {Johnson, Mark and Griffiths, Thomas L. and Goldwater, Sharon},
title = {Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models},
journal = {Advances in Neural Information Processing Systems 19},
year = {2007},
doi = {10.7551/mitpress/7503.003.0085},
url = {https://doi.org/10.7551/mitpress/7503.003.0085}
}TY - JOUR TI - Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models AU - Johnson, Mark AU - Griffiths, Thomas L. AU - Goldwater, Sharon PY - 2007 JO - Advances in Neural Information Processing Systems 19 DO - 10.7551/mitpress/7503.003.0085 UR - https://doi.org/10.7551/mitpress/7503.003.0085 ER -
Johnson, Mark et al.. “Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models.” Advances in Neural Information Processing Systems 19 (2007). https://doi.org/10.7551/mitpress/7503.003.0085.