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In recent years, dialectometry has gained interest among Catalan dialectologists. As a consequence, a specific dialectometric approach has been developed at the University of Barcelona, which aims at increasing the accuracy of final groupings by means of discriminating the predictable components of the language from its unpredictable ones. Another popular method to obtain dialect distances is the Levenshtein Distance (LD) which has never been applied to a Catalan corpus so far. The goal of this paper is to present the results of applying the LD to a corpus of Catalan linguistic data, and to compare the results from this analysis both with the results from Barcelona and the traditional classifications of Catalan dialectology.

This article investigates several linguistic changes which are ongoing in north-western Catalan using a contemporary corpus. We take advantage of a range of dialectometric methods that allow us to calculate and analyse the linguistic distance between varieties in apparent time from an aggregate perspective. Specifically, we pay attention to the process of structural dialect loss due to linguistic advergence to standard and eastern Catalan in many north-western Catalan dialects located in Catalonia (Spain) and Andorra. We also provide evidence that the dialect leveling taking place in these two areas strongly contrasts with the relative stability of the Catalan dialects on the other side of the Catalan–Aragonese border in Spain, where Catalan is not an official language. These opposite sociolinguistic situations (Catalonia and Andorra have strong language policies to support Catalan, whereas Aragon does not) have triggered a twofold process of vertical advergence between the Catalan spoken in Catalonia and Andorra towards the prestigious varieties, on the one hand; and of horizontal divergence between these dialects and those located in Aragon, on the other hand. This situation has notably strengthened the border differences between Aragon and Catalonia during the last 80 years. This article is one of the first attempts to study the border effects not only between regions belonging to different countries but also between different administrative regions ‘within’ the same country. In addition, we investigate the different roles of urban versus rural areas, providing support for the view that the spatial and hierarchical diffusion patterns are complementary.

In this study, we investigate which factors influence the linguistic distance of Catalan dialectal pronunciations from standard Catalan. We use pronunciations from three regions where the northwestern variety of the Catalan language is spoken (Catalonia, Aragon and Andorra). In contrast to Aragon, Catalan has an official status in both Catalonia and Andorra, which likely influences standardization. Because we are interested in the potentially large range of differences that standardization might promote, we examine 357 words in Catalan varieties and in particular their pronunciation distances with respect to the standard. In order to be sensitive to differences among the words, we fitted a generalized additive mixed-effects regression model to this data. This allows us to examine simultaneously the general (i.e. aggregate) patterns in pronunciation distance and to detect those words that diverge substantially from the general pattern. The results revealed higher pronunciation distances from standard Catalan in Aragon than in the other regions. Furthermore, speakers in Catalonia and Andorra, but not in Aragon, showed a clear standardization pattern, with younger speakers having dialectal pronunciations closer to the standard than older speakers. This clearly indicates the presence of a border effect within a single country with respect to word pronunciation distances. Since a great deal of scholarship focuses on single segment changes, we compare our analysis to the analysis of three segment changes that have been discussed in the literature on Catalan. This comparison revealed that the pattern observed at the word pronunciation level was supported by two of the three cases examined. As not all individual cases conform to the general pattern, the aggregate approach is necessary to detect global standardization patterns.

This is the report of a panel discussion held in connection with the special session on computational methods in dialectology at Methods XIII: Methods in Dialectology on 5 August, 2008 at the University of Leeds. We scheduled this panel discussion in order to reflect on what the introduction of computational methods has meant to our subfield of linguistics, dialectology (in alternative divisions of linguistic subfields also known as variationist linguistics), and whether the dialectologists' experience is typical of such introductions in other humanities studies. Let's emphasize that we approach the question as working scientists and scholars in the humanities rather than as methodology experts or as historians or philosophers of science, i.e. we wished to reflect on how the introduction of computational methods has gone in our own field in order to conduct our own future research more effectively, or alternatively, to suggest to colleagues in neighbouring disciplines which aspects of computational studies have been successful, which have not been, and which might have been introduced more effectively. Since we explicitly wished to reflect not only on how things have gone in dialectology, but also to compare our experiences to others, we invited panellists with broad experience in linguistics and other fields.

This paper describes an experiment on extracting Hungarian multi-word lexemes from a corpus, using statistical methods. Corpus preparation-the addition of POS tags and stems-was done automatically. From the corpus, verb+noun+casemark patterns were extracted as collocation candidates. Evaluation shows that the statistical methods used by Villada Moirón (2004a) to identify Dutch V + PP collocations, can also be applied to the Hungarian data. Some collocation types (such as verbal arguments) require special extraction methods, as explained in the evaluation section. Finally, we suggest that the extraction process can be further improved by a blend of statistical techniques with rule-based and dictionary-based methods.

Abstract This study is the first to use 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 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.

Information extraction systems learn patterns for extracting pairs instantiating a given relation from text. For instance, for the relation capital of a system might learn extraction patterns such as 'Arg1 is capital of Arg2', or'The embassador of arg2 was called back to Arg1'.