Lexical differences between Tuscan dialects and standard Italian

Accounting for geographic and sociodemographic variation using generalized additive mixed modeling

Bibliographic Data

ID7900225
AuthorsMartijn Wieling (0000-0003-0434-1526, Association for Computational Linguistics), Simonetta Montemagni (0000-0002-2953-8619, National Academies of Sciences, Engineering, and Medicine), John Nerbonne (0000-0002-3432-675X, Association for Computational Linguistics), R Harald Baayen (0000-0003-3178-3944, University of Alberta)
Year2014
Volume90
Issue3
Pages669-692
Publication date2014-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLanguage (JOURNAL)
Journal identifiersISSN: 0097-8507 • E-ISSN: 1535-0665
PublisherProject MUSE (PUBLISHER • US)
DOI10.1353/lan.2014.0064
OpenAlexW284501664
LanguageEN
Citations received25
References cited22

This study uses a generalized additive mixed-effects regression model to predict lexical differences in Tuscan dialects with respect to standard Italian. We used lexical information for 170 concepts used by 2,060 speakers in 213 locations in Tuscany. In our model, geographical position was found to be an important predictor, with locations more distant from Florence having lexical forms more likely to differ from standard Italian. In addition, the geographical pattern varied significantly for low- versus high-frequency concepts and older versus younger speakers. Younger speakers generally used variants more likely to match the standard language. Several other factors emerged as significant. Male speakers as well as farmers were more likely to use lexical forms different from standard Italian. In contrast, higher-educated speakers used lexical forms more likely to match the standard. The model also indicates that lexical variants used in smaller communities are more likely to differ from standard Italian. The impact of community size, however, varied from concept to concept. For a majority of concepts, lexical variants used in smaller communities are more likely to differ from the standard Italian form. For a minority of concepts, however, lexical variants used in larger communities are more likely to differ from standard Italian. Similarly, the effect of the other community- and speaker-related predictors varied per concept. These results clearly show that the model succeeds in teasing apart different forces influencing the dialect landscape and helps us to shed light on the complex interaction between the standard Italian language and the Tuscan dialectal varieties. In addition, this study illustrates the potential of generalized additive mixed-effects regression modeling applied to dialect data

Contrast (vision · Geography · Linguistics · Standard language · Variation (astronomy · Artificial Intelligence · Computer Science · Linguistic Variation and Morphology · Multilingual Education and Policy · Phonetics and Phonology Research · Psychology

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