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A Bayesian framework for word segmentation: Exploring the effects of context
Sharon Goldwater, Thomas L. Griffiths, Mark Johnson, 2009. Cognition.
A Bayesian model that segments unbroken speech or text into words, and shows that modelling context between words improves the segmentation.
What it's used for here
The surveys list it among unsupervised word-segmentation methods to run on the text without its spaces.
Cited by on this site 2 records
2 methods surveys
SRCSignal processing, inductive methods
Cite it
Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition. https://doi.org/10.1016/j.cognition.2009.03.008
@article{goldwater2009bayesian,
author = {Goldwater, Sharon and Griffiths, Thomas L. and Johnson, Mark},
title = {A Bayesian framework for word segmentation: Exploring the effects of context},
journal = {Cognition},
year = {2009},
doi = {10.1016/j.cognition.2009.03.008},
url = {https://doi.org/10.1016/j.cognition.2009.03.008}
}TY - JOUR TI - A Bayesian framework for word segmentation: Exploring the effects of context AU - Goldwater, Sharon AU - Griffiths, Thomas L. AU - Johnson, Mark PY - 2009 JO - Cognition DO - 10.1016/j.cognition.2009.03.008 UR - https://doi.org/10.1016/j.cognition.2009.03.008 ER -
Goldwater, Sharon et al.. “A Bayesian framework for word segmentation: Exploring the effects of context.” Cognition (2009). https://doi.org/10.1016/j.cognition.2009.03.008.