This paper presents a topic modeling approach to corpus-based dialectometry. Topic models are most often used in text mining to find latent structure in a collection of documents. They are based on the idea that frequently co-occurring words present the same underlying topic. In this study, topic models are used on interview transcriptions containing dialectal speech directly, without any annotations or preselected features. The transcriptions are modeled on complete words, on character n-grams, and after automatical segmentation. Data from three languages, Finnish, Norwegian, and Swiss German, are scrutinized. The proposed method is capable of discovering clear dialectal differences in all three datasets, while reflecting the differences between them. The method provides a significant simplification of the dialectometric workflow, simultaneously saving time and increasing objectivity. Using the method on non-normalized data could also benefit text mining, which is the traditional field of topic modeling.

This paper provides a new classification of Central–Southern Italian dialects using dialectometric methods. All varieties considered are analyzed and cast in a data set where homogeneous areas are evaluated according to a selected list of phonetic features. Using numerical evaluation of these features and the Manhattan distance, a linguistic distance rule is defined. On this basis, the classification problem is formulated as a clustering problem, and a k-means algorithm is used. Additionally, an ad-hoc rule is set to identify transitional areas, and silhouette analysis is used to select the most appropriate number of clusters. While meaningful results are obtained for each number of clusters, a nine-group classification emerges as the most appropriate. As the results suggest, this classification is less subjective, more precise, and more comprehensive than traditional ones based on selected isoglosses.

Describing specific dialect areas in terms of their lexis is an attractive idea now that the latest version of the English Dialect Dictionary Online (EDD Online 3.0, 2019) allows for quick and easy lexical retrievals of English dialect words of the Late Modern English period. This paper uses the Isle of Wight (I.W.) as a test case for putting such an idea into practice. The 137 words uniquely attributed in the EDD to I.W. are analyzed and interpreted in relation to the 1500-odd words used on I.W. alongside other areas of the UK. The paper informs the reader of the available query modes and discusses their pros and cons, quantifying and mapping the different numbers of isolated words in use on I.W. versus those unique to other English counties. The larger number of words that the island shared with the counties of the “mainland” will likewise be considered, thus allowing for first steps towards a “dialectometrical” analysis. The findings are related to the historical background of I.W., particularly in the 18th and 19th centuries.

This paper takes a quantitative perspective on data from the project Syntax hessischer Dialekte (SyHD), covering dialects in the German state of Hesse, an area with rich dialectal variation. Many previous dialectometric analyses abstracted away from intralocal variation (e.g., by only counting the most frequent variant at a location). In contrast, we do justice to intralocal variation by taking into account local frequency relations. The study shows that the border between Low German and Central German—one of the most important isoglosses in German dialectology—is not relevant for syntactic phenomena. At the same time, a comparison with character n-grams (a global measure of string similarity) reveals that the traditionally assumed dialect areas, primarily defined according to phonological developments, are still present in the twenty-first century data. Different from previous studies, our results are obtained from a uniform data base. Therefore, the differences between syntax and phonology cannot be due to variation in sampling, elicitation method, or time of elicitation.

This article is an analysis of linguistic survey data representing German dialects in Switzerland in 1933/34 based on the so-called Wenker sentences. The data are impressionistic in terms of applied phonetic transcriptions, which were produced by non-specialists using the Latin alphabet. Due to the lack of pre-defined standardization, the phonetic transcriptions are very heterogeneous. From a technical perspective, this leads to very noisy data, which is why the validity of the Wenker data in general and the Swiss Wenker data in particular has been questioned. Using methods from computational linguistics, we compare, for the first time, Wenker data with linguistic data collected at virtually the same time by linguistics professionals. Direct comparison with a sample from the published atlas of German-speaking Switzerland (SDS) reveals that despite the noisiness of the data, they nevertheless provide reliable information, e.g., in terms of the spatial structuring of Swiss dialects. The study is thus a successful pilot for other corpus-based studies dealing with unstructured Wenker data in other regions.

The use of loanwords is generally attributed to a social feature, like social prestige, and to semantic features, like the need to fill a lexical gap. However, few studies take into account variation in the use of loanwords within a speech community, and directly compare the frequency of loanwords from more than one source language. This paper contributes to research on lexical borrowing by comparing the distribution of loanwords from three different source languages in two large databases of dialect data. We take an onomasiological perspective, which allows us to gauge the frequency of borrowed lexical items vis-à-vis alternative expressions. Using Generalized Additive Mixed Modeling, we show that the usage of loanwords can only be explained by taking into account the interaction between semantics and geographical diffusion. Our analysis confirms that the patterns that occur almost exclusively reflect changes in socio-cultural history.