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.

Geolectal variation is often present in settings where one language is spoken across a vast geographic area. This can be found in phonological, morphosyntactic, and lexical features. For practical reasons, it is not always possible to conduct fieldwork in every single location of interest in order to obtain the full pattern of variation, and a sample of them must be chosen. We propose and test a method for sampling these locations, with the goal of obtaining a distribution of typological features representative of the whole area. We apply k-means and hierarchical clustering algorithms for defining this sample, based on their geographic distribution. We test our methods against simulated data with several spatial configurations, and also against real data from Circassian dialects (Northwest Caucasian). Our results show an efficiency significantly higher than random sampling for detecting this variation, which makes our method profitable to fieldworkers when designing their research.

The present study offers an examination of attitudes and perceptions of the Ukrainian language by respondents who have lived at least half of their life in Ukraine; they were asked to draw on a map of Ukraine where the most correct Ukrainian is spoken and where the Ukrainian that grates on one’s ears is spoken. Recruitment for the online survey was conducted by placing ads on several Ukrainian-language Facebook pages, along with a link to the survey. The findings presented are from a total of 90 analyzed surveys. Respondents’ maps were analyzed and compared using QGIS software. The research demonstrates that that there is a tug-of-war of correctness between Kyiv and Lviv. It also shows that there is an overall tendency of native speakers to evaluate the Transcarpathian region as the area that grates one’s ears.

This study uses a modified online version of the “draw-a-map” task and Garrett, Williams, and Evans’ (2005b) “keywords” methodology to explore the geospatial distribution of different accent and dialect labels and descriptors in Greater Manchester, UK. Specifically, we consider the distribution of the three most frequent labels related to “accentedness”: Broad, Strong, and Soft, as provided by 349 Greater Manchester residents. This analysis finds that these descriptors were clustered in separate areas of Greater Manchester, suggesting that they were being used to describe perceptually distinct varieties of English. In order to uncover the nuances in these folk-linguistic terms, we consider how they correlate with other concepts emerging from the dataset, finding that they are being used to differentiate between varieties with contrasting social associations. By combining innovative approaches, this study demonstrates how the subtleties of folk-linguistic modes of awareness can be uncovered through in-depth analysis of the terminology employed to describe linguistic variation on a very local scale. In so doing, it paves the way for further development of draw-a-map techniques that will enable similarly nuanced analysis in different regions, thus pushing the sub-discipline forward.