A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns (2022)

Abstract: Computational approaches in historical linguistics have been increasingly applied during the past decade and many new methods that implement parts of the traditional comparative method have been proposed. Despite these increased efforts, there are not many easy-to-use and fast approaches for the task of phonological reconstruction. Here we present a new framework that combines state-of-the-art techniques for automated sequence comparison with novel techniques for phonetic alignment analysis and sound correspondence pattern detection to allow for the supervised reconstruction of word forms in ancestral languages. We test the method on a new dataset covering six groups from three different language families. The results show that our method yields promising results while at the same time being not only fast but also easy to apply and expand.

## introduction phonological reconstruction is a technique by which words in ancestral languages, which may not even be reflected in any sources, are restored through the systematic comparison of descendant words (cognates) in descendant languages @xcite . traditionally, scholars apply the technique manually, but along with the recent quantitative turn in historical linguistics, scholars have increasingly tried to automate the procedure. recent automatic approaches for linguistic reconstruction, be they supervised or unsupervised, show two major problems. first, the underlying code is rarely made publicly available, which means that they cannot be further tested by applying them to new datasets. second, the methods have so far only been tested on a small amount of data from a limited number of language families. thus, @xcite report remarkable results on the reconstruction of oceanic languages, but the source code has never been published, and the method was never tested on additional datasets. @xcite report very promising results for the automated reconstruction of latin from romance languages, using a new test set derived from a dataset originally provided by @xcite , but they could only share part of the data, due to restrictions underlying the data by @xcite . @xcite experiment with the prediction of so far unelicited words in a small group of sino-tibetan languages, which they registered prior to verification @xcite , but they do not test the suitability of their approach for the reconstruction of ancestral languages. @xcite presents a complete pipeline by which words are clustered into cognate sets and ancestral word forms are reconstructed, but the method is only tested on a very small dataset of romance languages. with increasing efforts to unify and standardize lexical datasets from different sources @xcite , more and more datasets that could be used to test methods for automated linguistic reconstruction have become available. additionally, thanks to the huge progress which techniques for automated sequence comparison have made in the past decades @xcite @xcite , it is much easier today to combine existing methods into new frameworks that tackle individual tasks in computational historical linguistics. in this study, we present a new framework for automated linguistic reconstruction which combines state-of-the-art methods for automated sequence comparison with fast machine-learning techniques and test it on a newly compiled test set that covers multiple language families. ## materials the number of cross-linguistic datasets amenable for automated processing has been constantly increasing during the past years, as reflected specifically also in the development of standards for data representation that are increasingly used by while three of the datasets (bai, lalo, and purus) had been previously included into the lexibank collection, a repository of lexical datasets in cross-linguistic data formats @xcite , we converted the open part of the latin dataset by @xcite to cldf. additionally, we converted a selection of a smaller part of the data by @xcite to cldf and retrostandardized the data by @xcite . while all datasets provided forms for ancestral languages, not all datasets provided the direct links between these proto-forms and the reflexes in the descendant languages in the form of annotations indicating cognacy. while these were added manually for the karen data, using the edictor tool for etymological data curation @xcite , we used the automated method for partial cognate detection by @xcite to cluster proto-forms and reflexes into cognate sets for the data on bai, lalo, and purus. the datasets, along with their sources and some basic information regarding the number of languages (l), cognate sets (c), and word forms (w) are listed in ## results in order to evaluate the framework, we tested two classifiers, a support vector machine, and the cor- figure 5 compares the results for four coding techniques on individual datasets. as can be seem from the figure, the impact of the coding techniques varies quite drastically across datasets. this shows that it would be premature to rule out any of the techniques tested here directly, but rather calls for a careful selection of alignment enrichment techniques dependent on the language family one wants to investigate. ## conclusion in this study, we have presented a new framework for supervised phonological reconstruction, which is implemented in the form of a small python package. the new framework has the advantage of being easy to use, easy to extend, and fast to apply, while at the same time yielding promising results on a newly compiled collection of datasets from three different languages families. given that our framework can be easily extended, by varying the individual components of the worfklow, we hope that it will provide a solid basis for future work on phonological reconstruction, as well as the prediction of words from cognate reflexes @xcite @xcite in computational historical linguistics.