Antti Leino

Rule Discovery and Probabilistic Modeling for Onomastic Data

The naming of natural features, such as hills, lakes, springs, meadows etc., provides a wealth of linguistic information; the study of the names and naming systems is called onomastics. We consider a data set containing all names and locations of about 58,000 lakes in Finland. Using computational techniques, we address two major onomastic themes. First, we address the existence of local dependencies or repulsion between occurrences of names. For this, we derive a simple form of spatial association rules. The results partially validate and partially contradict results obtained by traditional onomastic techniques. Second, we consider the existence of relatively homogeneous spatial regions with respect to the distributions of place names. Using mixture modeling, we conduct a global analysis of the data set. The clusterings of regions are spatially connected, and correspond quite well with the results obtained by other techniques; there are, however, interesting differences with previous hypotheses.

Multivariate Analysis of Finnish Dialect Data—An Overview of Lexical Variation

During the process of writing a comprehensive dictionary of Finnish dialects, a large set of maps describing the regional distribution of the dialect words have been compiled in electronic form. In this article, we set out to analyse this corpus of data in order to gain new insight on the variation of Finnish dialects. We use a wide range of multivariate data analysis methods, including principal components analysis, independent components analysis, clustering, and multidimensional scaling. We explain how to preprocess the data to overcome the problem of uneven sampling caused by the way the data has been collected. We discuss the results obtained by these methods and compare them to the traditional view of Finnish dialect groups.

Comparison of Component Models in Analysing the Distribution of Dialectal Features

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.