languageR (version 1.5.0)

regularity: Regular and irregular Dutch verbs

Description

Regular and irregular Dutch verbs and selected lexical and distributional properties.

Usage

data(regularity)

Format

A data frame with 700 observations on the following 13 variables.

Verb

a factor with the verbs as levels.

WrittenFrequency

a numeric vector of logarithmically transformed frequencies in written Dutch (as available in the CELEX lexical database).

NcountStem

a numeric vector for the number of orthographic neighbors.

VerbalSynsets

a numeric vector for the number of verbal synsets in WordNet.

MeanBigramFrequency

a numeric vector for mean log bigram frequency.

InflectionalEntropy

a numeric vector for Shannon's entropy calculated for the word's inflectional variants.

Auxiliary

a factor with levels hebben, zijn and zijnheb for the verb's auxiliary in the perfect tenses.

Regularity

a factor with levels irregular and regular.

LengthInLetters

a numeric vector of the word's orthographic length.

FamilySize

a numeric vector for the number of types in the word's morphological family.

Valency

a numeric vector for the verb's valency, estimated by its number of argument structures.

NVratio

a numeric vector for the log-transformed ratio of the nominal and verbal frequencies of use.

WrittenSpokenRatio

a numeric vector for the log-transformed ratio of the frequencies in written and spoken Dutch.

References

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.

Examples

# 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)
# }