153 research outputs found

    Automated identification of borrowings in multilingual wordlists

    Get PDF
    Although lexical borrowing is an important aspect of language evolution, there have been few attempts to automate the identification of borrowings in lexical datasets. Moreover, none of the solutions which have been proposed so far identify borrowings across multiple languages. This study proposes a new method for the task and tests it on a newly compiled large comparative dataset of 48 South-East Asian languages from Southern China. The method yields very promising results, while it is conceptually straightforward and easy to apply. This makes the approach a perfect candidate for computer-assisted exploratory studies on lexical borrowing in contact areas

    Computational Approaches to Historical Language Comparison

    Get PDF
    The chapter discusses recently developed computational techniques providing concrete help in addressing various tasks in historical language comparison, focusing specifically on those tasks which are typically subsumed under the framework of the comparative method. These include the proof of relationship, cognate and correspondence detection, phonological reconstruction and sound law induction, and the reconstruction of evolutionary scenarios

    The potential of automatic word comparison for historical linguistics

    Get PDF
    The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection-although not perfect -could become an important component of future research in historical linguistics.As part of the GlottoBank Project, this work was supported by the Max Planck Institute for the Science of Human History and the Royal Society of New Zealand Marsden Fund grant 13¬UOA-121. This paper was further supported by the DFG research fellowship grant 261553824 “Vertical and lateral aspects of Chinese dialect history”(JML), and the Australian Research Council’s Discovery Projects funding scheme (project number DE120101954, SJG)

    Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?

    Get PDF
    We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cog- nate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We con- clude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family’s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies

    Annotating Cognates in Phylogenetic Studies of South-East Asian Languages

    Get PDF
    Compounding and derivation are frequent in South-East Asian languages. Consequently, words in different languages are often only partially cognate, sharing only a few but not all morphemes. While partial cognates do not constitute a problem for the phonological reconstruction of individual morphemes, they are problematic when it comes to phylogenetic reconstruction based on comparative wordlists. Here, we review the current practice of preparing cognate-coded wordlists and develop new approaches that make the process of cognate annotation more transparent. Comparing four methods by which partial cognate judgments can be converted to cognate judgments for whole words on a newly annotated dataset of 19 Chinese dialect varieties, we find that the choice of the conversion method has a large impact on the inferred tree topologies. We conclude that scholars should take cognate judgments in languages in which compounding and derivation are frequent with great care, and recommend to assign cognates always transparently

    Computer-Assisted Language Comparison: State of the Art

    Get PDF
    Historical language comparison opens windows onto a human past, long before the availability of written records. Since traditional language comparison within the framework of the comparative method is largely based on manual data comparison, requiring the meticulous sifting through dictionaries, word lists, and grammars, the framework is difficult to apply, especially in times where more and more data have become available in digital form. Unfortunately, it is not possible to simply automate the process of historical language comparison, not only because computational solutions lag behind human judgments in historical linguistics, but also because they lack the flexibility that would allow them to integrate various types of information from various kinds of sources. A more promising approach is to integrate computational and classical approaches within a computer-assisted framework, “neither completely computer-driven nor ignorant of the assistance computers afford” [1, p. 4]. In this paper, we will illustrate what we consider the current state of the art of computer-assisted language comparison by presenting a workflow that starts with raw data and leads up to a stage where sound correspondence patterns across multiple languages have been identified and can be readily presented, inspected, and discussed. We illustrate this workflow with the help of a newly prepared dataset on Hmong-Mien languages. Our illustration is accompanied by Python code and instructions on how to use additional web-based tools we developed so that users can apply our workflow for their own purposes

    Sequence comparison in computational historical linguistics

    Get PDF
    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)

    Challenges of Annotation and Analysis in Computer-Assisted Language Comparison: A Case Study on Burmish Languages

    Get PDF
    The use of computational methods in comparative linguistics is growing in popularity. The increasing deployment of such methods draws into focus those areas in which they remain inadequate as well as those areas where classical approaches to language comparison are untransparent and inconsistent. In this paper we illustrate specific challenges which both computational and classical approaches encounter when studying South-East Asian languages. With the help of data from the Burmish language family we point to the challenges resulting from missing annotation standards and insufficient methods for analysis and we illustrate how to tackle these problems within a computer-assisted framework in which computational approaches are used to pre-analyse the data while linguists attend to the detailed analyses

    An automated framework for fast cognate detection and Bayesian phylogenetic inference in computational historical linguistics

    Get PDF
    We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics
    corecore