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The Meta-Science of Adult Statistical Word Segmentation: Part 1
Author(s) -
Joshua K. Hartshorne,
Lauren Skorb,
Sven L. Dietz,
Caitlin Garcia,
Gina Iozzo,
Katie Lamirato,
James Ledoux,
Jesse Mu,
Kara Murdock,
Jon Ravid,
Alyssa Savery,
James Spizzirro,
Kelsey Trimm,
Kendall D. van Horne,
Juliani Vidal
Publication year - 2019
Publication title -
collabra psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.444
H-Index - 10
ISSN - 2474-7394
DOI - 10.1525/collabra.181
Subject(s) - replicate , segmentation , robustness (evolution) , computer science , natural language processing , artificial intelligence , statistical power , word (group theory) , statistics , text segmentation , machine learning , mathematics , biochemistry , geometry , gene , chemistry
We report the first set of results in a multi-year project to assess the robustness – and the factors promoting robustness – of the adult statistical word segmentation literature. This includes eight total experiments replicating six different experiments. The purpose of these replications is to assess the reproducibility of reported experiments, examine the replicability of their results, and provide more accurate effect size estimates. Reproducibility was mixed, with several papers either lacking crucial details or containing errors in the description of method, making it difficult to ascertain what was done. Replicability was also mixed: although in every instance we confirmed above-chance statistical word segmentation, many theoretically important moderations of that learning failed to replicate. Moreover, learning success was generally much lower than in the original studies. In the General Discussion, we consider whether these differences are due to differences in subject populations, low power in the original studies, or some combination of these and other factors. We also consider whether these findings are likely to generalize to the broader statistical word segmentation literature.

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