Regular and irregular Dutch verbs and selected lexical and distributional properties.
A data frame with 700 observations on the following 13 variables.
a factor with the verbs as levels.
a numeric vector of logarithmically transformed frequencies in written Dutch (as available in the CELEX lexical database).
a numeric vector for the number of orthographic neighbors.
a numeric vector for the number of verbal synsets in WordNet.
a numeric vector for mean log bigram frequency.
a numeric vector for Shannon's entropy calculated for the word's inflectional variants.
a factor with levels
zijnheb for the verb's auxiliary in the perfect tenses.
a factor with levels
a numeric vector of the word's orthographic length.
a numeric vector for the number of types in the word's morphological family.
a numeric vector for the verb's valency, estimated by its number of argument structures.
a numeric vector for the log-transformed ratio of the nominal and verbal frequencies of use.
a numeric vector for the log-transformed ratio of the frequencies in written and spoken Dutch.
Baayen, R. H. and Moscoso del Prado Martin, F. (2005) Semantic density and past-tense formation in three Germanic languages, Language, 81, 666-698.
Tabak, W., Schreuder, R. and Baayen, R. H. (2005) Lexical statistics and lexical processing: semantic density, information complexity, sex, and irregularity in Dutch, in Kepser, S. and Reis, M., Linguistic Evidence - Empirical, Theoretical, and Computational Perspectives, Berlin: Mouton de Gruyter, pp. 529-555.
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## Not run: data(regularity) # ---- predicting regularity with a logistic regression model library(rms) regularity.dd = datadist(regularity) options(datadist = 'regularity.dd') regularity.lrm = lrm(Regularity ~ WrittenFrequency + rcs(FamilySize, 3) + NcountStem + InflectionalEntropy + Auxiliary + Valency + NVratio + WrittenSpokenRatio, data = regularity, x = TRUE, y = TRUE) anova(regularity.lrm) # ---- model validation validate(regularity.lrm, bw = TRUE, B = 200) pentrace(regularity.lrm, seq(0, 0.8, by = 0.05)) regularity.lrm.pen = update(regularity.lrm, penalty = 0.6) regularity.lrm.pen # ---- a plot of the partial effects plot(Predict(regularity.lrm.pen)) # predicting regularity with a support vector machine library(e1071) regularity$AuxNum = as.numeric(regularity$Auxiliary) regularity.svm = svm(regularity[, -c(1,8,10)], regularity$Regularity, cross=10) summary(regularity.svm) ## End(Not run)
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