Modeling human performance in statistical word segmentation
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Tenenbaum_Modeling human performance.pdf
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Author(s)
Frank, Michael C.
Goldwater, Sharon
Griffiths, Thomas L.
Tenenbaum, Joshua B.
Date Issued
July 2010
Journal
Cognition
Publisher
Elsevier
Citation
Frank, Michael C., Sharon Goldwater, Thomas L. Griffiths, and Joshua B. Tenenbaum. “Modeling Human Performance in Statistical Word Segmentation.” Cognition 117, no. 2 (November 2010): 107–125.
Version
Author's final manuscript
Abstract
The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cognition.2010.07.005
