Fishing for Exactness

dc.creatorPedersen, Ted
dc.date1996-08-16
dc.date.accessioned2026-07-07T09:10:36Z
dc.date.available2026-07-07T09:10:36Z
dc.descriptionStatistical methods for automatically identifying dependent word pairs (i.e. dependent bigrams) in a corpus of natural language text have traditionally been performed using asymptotic tests of significance. This paper suggests that Fisher's exact test is a more appropriate test due to the skewed and sparse data samples typical of this problem. Both theoretical and experimental comparisons between Fisher's exact test and a variety of asymptotic tests (the t-test, Pearson's chi-square test, and Likelihood-ratio chi-square test) are presented. These comparisons show that Fisher's exact test is more reliable in identifying dependent word pairs. The usefulness of Fisher's exact test extends to other problems in statistical natural language processing as skewed and sparse data appears to be the rule in natural language. The experiment presented in this paper was performed using PROC FREQ of the SAS System.
dc.description13 pages - postscript
dc.identifierhttps://arxiv.org/abs/cmp-lg/9608010
dc.identifierhttp://arxiv.org/abs/cmp-lg/9608010
dc.identifierProceedings of the South-Central SAS Users Group Conference (SCSUG-96), Austin, TX, Oct 27-29, 1996
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151394
dc.subjectComputation and Language
dc.titleFishing for Exactness
dc.typetext

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