Lexical distributional measures for 2233 English monomorphemic words. This dataset provides
a subset of the data available in the dataset
A data frame with 2233 observations on the following 24 variables.
a factor with 2284 words.
numeric vector with log-transformed lemma frequency in the CELEX lexical database.
numeric vector with the logged ratio of written frequency (CELEX) to spoken frequency (British National Corpus).
numeric vector with log morphological family size.
numeric vector with derivational entropy.
numeric vector with inflectional entropy.
numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed.
numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed as part of a compound.
numeric vector with length of the word in letters.
numeric vector with orthographic neighborhood density.
numeric vector with mean log bigram frequency.
numeric vector with log frequency of initial diphone.
numeric vector with type count of orthographic neighbors.
numeric vector with token count of orthographic neighbors.
numeric vector with type count of phonological neighbors.
numeric vector with token count of phonological neighbors.
numeric vector with type counts of consistent words.
numeric vector with token counts of consistent words.
numeric vector with type count of forward inconsistent words.
numeric vector with token count of forward inconsistent words.
numeric vector with type count of backward inconsistent words.
numeric vector with token count of backward inconsistent words
a numeric vector with the count of forward inconsistent words with nonzero frequency.
a numeric vector with the logarithmically transformed ratio of the noun and verb frequencies.
Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.
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## Not run: data(lexicalMeasures) data(lexicalMeasuresDist) library(rms) library(cluster) plot(varclus(as.matrix(lexicalMeasures[,-1]))) lexicalMeasures.cor = cor(lexicalMeasures[,-1], method = "spearman")^2 lexicalMeasures.dist = dist(lexicalMeasures.cor) pltree(diana(lexicalMeasures.dist)) data(lexicalMeasuresClasses) x = data.frame(measure = rownames(lexicalMeasures.cor), cluster = cutree(diana(lexicalMeasures.dist), 5), class = lexicalMeasuresClasses$Class) x = x[order(x$cluster), ] x ## End(Not run)
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