Fruehwald, Josef

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     Edinburgh Research Explorer                                      Choice of word frequency norms can dramatically affectinferenceCitation for published version:Fruehwald, J 2017, 'Choice of word frequency norms can dramatically affect inference' 11th UK LanguageVariation and Change (UKLVC), Cardiff, United Kingdom, 29/08/17 - 31/08/17, .Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Choice of Word Frequency Norms can Dramatically Effect Inference. Josef FruehwaldSome factors influencing variation are observable, and others must be estimated./n//ay/F1 = 520hz 200ms/s/nasalcoronal80msvoicelessfricative430msNICE- valence- lexical neighborhood density- frequencyDifferent estimates may be correlated,Corr:0.71Corr:0.731Corr:0.7buck_zipf celex_zipf subtlex_zipfbuck_zipfcelex_zipfsubtlex_zipf2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 70.000.250.500.751.00234567234567but are they interchangable?Case study 1: TD Deletion Case study 2: /ay/ raisingBuckeye Corpus Philadelphia Neighborhood CorpusTD Deletion Data: Monomorphemes Frequency Norms: Zipf Scaledwest [wɛst] [wɛs]~child [tʃɑɪld] [tʃɑɪl] ~log10(frequency per million words) + 3Modeltd ~ zipfscore + (1 | Word) + (zipfscore | Speaker)Frequency Norm Estimated EﬀectWithin CorpusCelexSubtlex−0.29−0.15−0.10x Within Corpus0.520.34Frequency Norm Estimated EﬀectWithin CorpusCelexSubtlex−0.599−0.006−0.302x Within Corpus0.010.51Regression Results:DiscussionThe three different frequency norms result in very different estimated frequency effects. The within corpus frequency norm estimated a frequency effect twice to 100 times the size of the others./Data: /ay/ Raising from the PNC Model:Why the differences?The biggest difference between these norms is their estimates of low frequency words. Recommendation: Use the norms with the best low frequency word estimates. 0.50.60.70.80.91.02 3 4 5 6Zipf ScaleProbability /td/ retentionnormwithin corpuscelexsubtlexrightnice[ɹɑɪt] [ɹʌit][nɑɪs] [nʌis]~ ~6,691 Tokens 18,236 Tokens18,608 F1 EstimatesF1 ~ decade * zipfscore + (decade | Word) + (zipfscore | Speaker)Regression Results:Within Corpusinterceptdecade:freqfreqdecadeestimate CI0.68−0.12−0.03−0.006(  0.6, (−0.13, (−0.09,   (−0.01,       0.76)−0.10)0.04)0.01)Celexinterceptdecade:freqfreqdecadeestimate CI0.64−0.12−0.09−0.001(  0.57, (−0.13, (−0.15,   (−0.01,      0.71)−0.10)−0.01)0.01)Subtlexinterceptdecade:freqfreqdecadeestimate CI0.67−0.12−0.05−0.0003(  0.6, (−0.13, (−0.12,   (−0.01,       0.74)−0.10)0.02)0.01)DiscussionThis time, the within-corpus frequency norm estimates the smallest frequency effect, but two of the norms don't have a reliable effect, while the remaining one does.-1.0-0.50.00.51.0(1,2] (2,3] (3,4] (4,5] (5,6] (6,7]bincorpairsubtlex buckeyesubtlex celexCorrelation within frequency bins2 3 4 5Zipf scale (subtlex)Subtlex frequency of frequency = 1 in BuckeyeReferencesBaayen, R. H., Piepenbrock, R. & Gulikers, L. (1995). The CELEX lexical database (Release 2, CD-ROM), LDC catalogue No.: LDC96L14, Philadelphia: Linguistic Data Consortium, University of Pennsylvania.Brysbaert, M., & New, B. (2009). Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–90. https://doi.org/10.3758/BRM.41.4.977Fruehwald, J. (2016). The early influence of phonology on a phonetic change. Language, 92(2), 376–410. https://doi.org/10.1353/lan.2016.0041Hay, J. B., Pierrehumbert, J. B., Walker, A. J., & LaShell, P. (2015). Tracking word frequency effects through 130years of sound change. Cognition, 139, 83–91. https://doi.org/10.1016/j.cognition.2015.02.012Labov, W., & Rosenfelder, I. (2011). The Philadelphia Neighborhood Corpus.Pitt, M. A., Dilley, L., Johnson, K., Kiesling, S., Raymond, W., Hume, E., & Fosler-Lussier, E. (2007). Buckeye Corpus of Conversational Speech (2nd release). Columbus, OH. Retrieved from www.buckeyecorpus.osu.eduTamminga, M. (2014). Persistence in the Production of Linguistic Variation. University of Pennsylvania.Van Heuven, W.J.B., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). Subtlex-UK: A new and improved word frequency database for British English. Quarterly Journal of Experimental Psychology, 67, 1176-1190

Choice of word frequency norms can dramatically affect inference

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Choice of word frequency norms can dramatically affect inference

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