Representing and Computing Uncertainty in Phonological Reconstruction

Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formalization makes it possible to address both the representation and the computation of uncertainty. Building on recent advances in supervised phonological reconstruction, during which an algorithm learns how to reconstruct words in a given proto-language relying on previously annotated data, and inspired by improved methods for automated word prediction from cognate sets, we present a new framework that allows for the representation of uncertainty in linguistic reconstruction and also includes a workflow for the computation of fuzzy reconstructions from linguistic data.Comment: To appear in: Proceedings of the 4th Workshop on Computational Approaches to Historical Language Chang

CLICS² An Improved Database of Cross-Linguistic Colexifications : Assembling Lexical Data with the Help of Cross-Linguistic Data Formats

International audienceThe Database of Cross-Linguistic Colexifications (CLICS), has established a computer-assisted framework for the interactive representation of cross-linguistic colexification patterns. In its current form, it has proven to be a useful tool for various kinds of investigation into cross-linguistic semantic associations , ranging from studies on semantic change, patterns of conceptualization, and linguistic pale-ontology. But CLICS has also been criticized for obvious shortcomings, ranging from the underlying dataset, which still contains many errors, up to the limits of cross-linguistic colexification studies in general. Building on recent standardization efforts reflected in the Cross-Linguistic Data Formats initiative (CLDF) and novel approaches for fast, efficient, and reliable data aggregation, we have created a new database for cross-linguistic colexifications, which not only supersedes the original CLICS database in terms of coverage but also offers a much more principled procedure for the creation, curation and aggregation of datasets. The paper presents the new database and discusses its major features

A digital, retro-standardized edition of the Tableaux phonétiques des patois Suisses romands (TPPSR)

This study presents a digital, retro-standardized edition of the Tableaux Phonétiques des Patois Suisses Romands (TPPSR), an early collection of lexical dialect data of the Suisse romande, which was compiled by Louis Gauchat, Jules Jeanqaquet, and Ernest Tappolet in the beginning of the 20th century and later published in 1925. While the plan of Gauchat and his collaborators to turn their data into a dialect atlas could never be realized for the lack of funding, we show how consistent techniques for digitization, accompanied by transparent approaches to retro-standardization can be used to turn the original data of the TPPSR into a modern interactive dialect atlas. The dialect atlas is not only publicly available in the form of a web-based application, but also in the form of a dataset that offers the data in standardized, human- and machine-readable form

Towards a sustainable handling of interlinear-glossed text in language documentation

While the amount of digitally available data on the worlds' languages is steadily increasing, with more and more languages being documented, only a small proportion of the language resources produced are sustainable. Data reuse is often difficult due to idiosyncratic formats and a negligence of standards that could help to increase the comparability of linguistic data. The sustainability problem is nicely reflected in the current practice of handling interlinear-glossed text, one of the crucial resources produced in language documentation. Although large collections of glossed texts have been produced so far, the current practice of data handling makes data reuse difficult. In order to address this problem, we propose a first framework for the computer-assisted, sustainable handling of interlinear-glossed text resources. Building on recent standardization proposals for word lists and structural datasets, combined with state-of-the-art methods for automated sequence comparison in historical linguistics, we show how our workflow can be used to lift a collection of interlinear-glossed Qiang texts (an endangered language spoken in Sichuan, China), and how the lifted data can assist linguists in their research

Sequence comparison in computational historical linguistics

With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well.This research was supported by the European Research Council Starting Grant ‘Computer-Assisted Language Comparison’ (Grant CALC 715618, J.M.L., T.T.) and the Australian Research Council’s Centre of Excellence for the Dynamics of Language (Australian National University, Grant CE140100041, S.J.G.). As part of the GlottoBank project (http://glottobank.org), this work was further supported by the Department of Linguistic and Cultural Evolution of the Max Planck Institute for the Science of Human History (Jena) and the Royal Society of New Zealand (Marsden Fund, Grant 13-UOA-121)

Pofatu, a curated and open-access database for geochemical sourcing of archaeological materials

Compositional analyses have long been used to determine the geological sources of artefacts.Geochemical “fingerprinting” of artefacts and sources is the most effective way to reconstruct strategies of raw material and artefact procurement, exchange or interaction systems, and mobility patterns during prehistory. The efficacy and popularity of geochemical sourcing has led to many projects using various analytical techniques to produce independent datasets. In order to facilitate access to this growing body of data and to promote comparability and reproducibility in provenance studies, we designed Pofatu, the first online and open-access database to present geochemical compositions and contextual information for archaeological sources and artefacts in a form that can be readily accessed by the scientific community. This relational database currently contains 7759 individual samples from archaeological sites and geological sources across the Pacific Islands. Each sample is comprehensively documented and includes elemental and isotopic compositions, detailed archaeological provenance, and supporting analytical metadata, such as sampling processes, analytical procedures, and quality control

Sequence comparison in computational historical linguistics

With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well

The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes

This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration