A Primer on Automatic Inference of Sound Correspondence Patterns (3): Extended Experiments with Alignments from the Tableaux Phonétiques des Patois Suisses Romands

Having illustrated how a quick correspondence pattern analysis can be done with help of readily formatted data and the EDICTOR tool alone, it is now time to show how we can use the LingRex package in order to carry out a full-fledged correspondence pattern analysis. While EDICTOR uses a simple algorithm that is based on sorting the patterns, the Python algorithm for correspondence pattern detection, which is described in detail in List (2019), uses a greedy approach inspired by the Welsh-Powell algorithm for graph coloring (Welsh and Powell 1967), in order to cluster all alignment sites in the data into clusters which are compatible with each other.

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A Primer on Automatic Inference of Sound Correspondence Patterns (2): Initial Experiments with Alignments from the Tableaux Phonétiques des Patois Suisses Romands

Following up on my announcement to present in more detail how the algorithms for automatic correspondence pattern detection can be applied, this post introduces the preliminary preparations needed to run a first experiment with aligned data. In order to avoid that we have to align a dataset completely from scratch, we make use of already aligned data from the Tableaux Phonétiques des Patois Suisses Romands by Gauchat et al. (1925), which were originally aligned for the study in List (2014) and later published as part of the Benchmark Database of Phonetic Alignments(List and Prokić 2014). In this post, I will introduce how we can harvest the alignments from this dataset with help of LingPy, and later analyze them with help of the sound correspondence pattern algorithms.

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A Primer on Automatic Inference of Sound Correspondence Patterns (1): Introduction

After about three years of work on the matter, I have managed (with help of many colleagues who helped in testing) to develop a first approach for the automatic inference of sound correspondence patterns, which will soon be published with Computational Linguistics (List 2019). The key task which this algorithm solves is to take aligned data as input and to compute explicit sound correspondence patterns from the alignments.

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