The volume we are introducing here contains a selection of the papers presented at a special track on computational techniques for studying language variation held at The Thirteenth International Conference on Methods in Dialectology in Leeds on 4–5 August 2008. We are grateful to the conference organisers, Nigel Armstrong, Joan Beal, Fiona Douglas, Barry Heselwood, Susan Lacey, Ann Thompson, and Clive Upton for their cooperation in our organisation of the event. We likewise owe thanks to the referees of the present volume, who we are pleased to acknowledge explicitly: Agnes de Bie, Roberto Bolognesi, David Britain, Cynthia Clopper, Ken Decker, Anders Eriksson, Hans Goebl, Stefan Grondelaers, Carlos Gussenhoven, Nynke de Haas, Frans Hinskens, Angela Kluge, Gitte Kristiansen, Alexandra Lenz, Maarten Mous, Hermann Niebaum, Lisa Lena Opas-Hänninen, Petya Osenova, John Paolillo, Louis Pols, Helge Sandøy, Bob Shackleton, Felix Schaeffler, Marco Spruit, Rint Sybesma, Nathan Vaillette, Gunther de Vogelaer, and Esteve Valls.

The conference track consisted of 24 papers and posters, including a keynote address by Vincent van Heuven on phonetic techniques for studying variation and comprehensibility. Fourteen contributions were selected for publication in this special issue of the International Journal for Humanities and Arts Computing, including van Heuven's. In addition the conference track featured a panel session reflecting on the introduction of computational techniques to the study of language variation and more generally, on computing and the humanities. We have prepared a report on the panel session for publication here as well.

Abstract In the present investigation, the intelligibility of 17 Scandinavian language varieties and standard Danish was assessed among young Danes from Copenhagen. In addition, distances between standard Danish and each of the 17 varieties were measured at the lexical level and at different phonetic levels. In order to determine how well these linguistic levels can predict intelligibility, we correlated the intelligibility scores with the linguistic distances and we carried out a number of regression analyses. The results show that for this particular set of closely related language varieties phonetic distance is a better predictor of intelligibility than lexical distance. Consonant substitutions, vowel insertions and vowel shortenings contribute significantly to the prediction of intelligibility.

INTRODUCTION

Gooskens (2007) correlated lexical and phonetic distances with mutual intelligibility scores for the Mainland Scandinavian standard languages, Danish, Norwegian and Swedish. Subjects from different places in Denmark, Norway and Sweden listened to the two standard languages spoken in the neighbouring countries and linguistic distances were measured between the language varieties of the listeners and the test languages. In total there were 18 mean intelligibility scores and 18 corresponding linguistic distances. The distances were measured at the two linguistic levels that are generally taken to be most important for mutual intelligibility in Scandinavia, namely the lexical and the phonetic level (Delsing and Lundin Åkesson, 2005; Torp, 1998). The results showed a high correlation between intelligibility scores and phonetic distances (r = −.80, p < .01).

Abstract Component models such as factor analysis can be used to analyse spatial distributions of a large number of different features – for instance the isogloss data in a dialect atlas, or the distributions of ethnological or archaeological phenomena – with the goal of finding dialects or similar cultural aggregates. However, there are several such methods, and it is not obvious how their differences affect their usability for computational dialectology. We attempt to tackle this question by comparing five such methods using two different dialectological data sets. There are some fundamental differences between these methods, and some of these have implications that affect the dialectological interpretation of the results.

INTRODUCTION

Languages are traditionally subdivided into geographically distinct dialects, although any such division is just a coarse approximation of a more fine-grained variation. This underlying variation is usually visualised in the form of maps, where the distribution of various features is shown as isoglosses. It is possible to view dialectal regions, in this paper also called simply dialects, as combinations of the distribution areas of these features, where the features have been weighted in such a way that the differences between the resulting dialects are as sharp as possible. Ideally, dialect borders are drawn where several isoglosses overlap.

As more and more dialectological data is available in electronic form, it is becoming increasingly attractive to apply computational methods to this problem. One way to do this is to use clustering methods (e.g. Kaufman and Rousseeuw, 1990), especially as such methods have been used in dialectometric studies (e.g. Heeringa and Nerbonne, 2002; Moisl and Jones, 2005).

Abstract The research reported in this paper departs from most previous work in dialectometry in several ways. Empirically, it draws on frequency vectors derived from naturalistic corpus data and not on discrete atlas classifications. Linguistically, it is concerned with morphosyntactic (as opposed to lexical or pronunciational) variability. Methodologically, it marries the careful analysis of dialect phenomena in authentic, naturalistic texts to aggregational-dialectometrical techniques. Two research questions guide the investigation: First, on methodological grounds, is corpus-based dialectometry viable at all? Second, to what extent is morphosyntactic variation in nonstandard British dialects patterned geographically? By way of validation, findings will be matched against previous work on the dialect geography of Great Britain.

INTRODUCTION

The overarching aim in this study is to provide a methodological sketch of how to blend philologically responsible corpus-based research with aggregational-dialectometrical analysis techniques. The bulk of previous research in dialectometry has focussed on phonology and lexis (however, for work on Dutch dialect syntax see Spruit 2005, 2006, 2008, Spruit et al. t.a.). Moreover, orthodox dialectometry draws on linguistic atlas classifications as its primary data source. The present study departs from these traditions in several ways. It endeavours, first, to measure aggregate morphosyntactic distances and similarities between traditional dialects in the British Isles. Second, the present study does not rely on atlas data but on frequency information deriving from a careful analysis of language use in authentic, naturalistic texts. This is another way of saying that the aggregate analysis in this paper is frequency-based, an approach that contrasts with atlas-based dialectometry, which essentially relies on categorical input data.

Abstract The paper illustrates the results of a correlation study focusing on linguistic variation in an Italian region, Tuscany. By exploiting a multilevel representation scheme of dialectal data, the study analyses attested patterns of phonetic and morpho-lexical variation with the aim of testing the degree of correlation between a) phonetic and morpho-lexical variation, and b) linguistic variation and geographic distance. The correlation analysis was performed by combining two complementary approaches proposed in dialectometric literature, namely by computing both global and place-specific correlation measures and by inspecting their spatial distribution. Achieved results demonstrate that phonetic and morpho-lexical variations in Tuscany seem to follow a different pattern than encountered in previous studies.

INTRODUCTION

It is a well-known fact that different types of features contribute to the linguistic distance between any two locations, which can differ for instance with respect to the word used to denote the same object or the phonetic realisation of a particular word. Yet, the correlation between different feature types in defining patterns of dialectal variation represents an area of research still unexplored. In traditional dialectology, there is no obvious way to approach this matter beyond fairly superficial and impressionistic observations. The situation changes if the same research question is addressed in the framework of dialectometric studies, where it is possible to measure dialectal distances with respect to distinct linguistic levels and to compute whether and to what extent observed distances correlate.

Nonmonotonic or Defeasible Reasoning

Those AI researchers called logicists, who favor the use of logical languages for representing knowledge and the use of logical methods for reasoning, acknowledge one problem with ordinary logic; namely, it is monotonic. By that they mean that the set of logical conclusions that can be drawn from a set of logical statements does not decrease as more statements are added to the set. If one could prove a statement from a given knowledge base, one could still prove that same statement (with the very same proof!) when more knowledge is added.

Yet, much human reasoning does not seem to work that way – a fact well noticed (and celebrated) by AI's critics. Often, we jump to a conclusion using the facts we happen to have, together with reasonable assumptions, and then have to retract that conclusion when we learn some new fact that contradicts the assumptions. That style of reasoning is called nonmonotonic or defeasible (meaning “capable of being made or declared null and void”) because new facts might require taking back something concluded before.

One can even find examples of nonmonotonic reasoning in children's stories. In That's Good! That's Bad!, by Margery Cuyler, a little boy floats high into the sky holding on to a balloon his parents bought him at the zoo. “Wow! Oh, that's good,” the story goes.

For a system to be intelligent, it must have knowledge about its world and the means to draw conclusions from, or at least act on, that knowledge. Humans and machines alike therefore must have ways to represent this needed knowledge in internal structures, whether encoded in protein or silicon. Cognitive scientists and AI researchers distinguish between two main ways in which knowledge is represented: procedural and declarative. In animals, the knowledge needed to perform a skilled action, such as hitting a tennis ball, is called procedural because it is encoded directly in the neural circuits that coordinate and control that specific action. Analogously, automatic landing systems in aircraft contain within their control programs procedural knowledge about flight paths, landing speeds, aircraft dynamics, and so on. In contrast, when we respond to a question, such as “How old are you?,” we answer with a declarative sentence, such as “I am twenty-four years old.” Any knowledge that is most naturally represented by a declarative sentence is called declarative.

In AI research (and in computer science generally), procedural knowledge is represented directly in the programs that use that knowledge, whereas declarative knowledge is represented in symbolic structures that are more-or-less separate from the many different programs that might use the information in those structures. Examples of declarative-knowledge symbol structures are those that encode logical statements (such as those McCarthy advocated for representing world knowledge) and those that encode semantic networks (such as those of Raphael or Quillian). Typically, procedural representations, specialized as they are to particular tasks, are more efficient (when performing those tasks), whereas declarative ones, which can be used by a variety of different programs, are more generally useful.

Computer scientists have developed various ways to put together large programs consisting of many specialist subprograms. The traditional framework that controls the running of most programs involves having a main program that runs through its instructions step by step, retrieving from and storing data in memory, executing various operations on such data, and taking other allowed actions. Some of the instructions in the main program might be to “call” a subprogram, handing control over to it. The subprograms, in turn, can call other subprograms, and so on. After a subprogram finishes doing what it has been called to do, overall control returns to the program that called it, which might then call another subprogram, and so on until control finally returns to the main program. Eventually, the main program can finally quit running, having accomplished all that it was supposed to do, or it can continue running (in principle, forever) because, like a program that makes airline reservations on demand for all who use it, its work is never done. This scheme is the so-called von Neumann architecture.

There are many elaborations on this general idea. “Interrupts” can be included in programs and subprograms. These are ever watchful for special conditions within the computer system itself or in the environment – conditions, which if met, would call for control to be transferred immediately to programs that are able to handle such conditions. Computer operating systems, for example, depend on interrupts to be responsive to user inputs and to other things going on with the computer hardware.