Networks of lexical borrowing and lateral gene transfer in language and genome evolution

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution

Investigating diachronic trends in phonological inventories using BDPROTO

Here we present an expanded version of bdproto, a database comprising phonological inventory data from 257 ancient and reconstructed languages. These data were extracted from historical linguistic reconstructions and brought together into a single unified, normalized, accessible, and Unicode-compliant language resource. This dataset is publicly available and we aim to engage language scientists doing research on language change and language evolution. Furthermore, we identify a hitherto undiscussed temporal bias that complicates the simple comparison of ancient and reconstructed languages with present-day languages. Due to the sparsity of the data and the absence of statistical and computational methods that can adequately handle this bias, we instead directly target rates of change within and across families, thereby providing a case study to highlight bdproto’s research viability; using phylogenetic comparative methods and high-resolution language family trees, we investigate whether consonantal and vocalic systems differ in their rates of change over the last 10,000 years. In light of the compilation of bdproto and the findings of our case study, we discuss the challenges involved in comparing the sound systems of reconstructed languages with modern day languages

Investigating diachronic trends in phonological inventories using BDPROTO

Here we present an expanded version of BDPROTO, a database comprising phonological inventory data from 257 ancient and reconstructed languages. These data were extracted from historical linguistic reconstructions and brought together into a single unified, normalized, accessible, and Unicode-compliant language resource. This dataset is publicly available and we aim to engage language scientists doing research on language change and language evolution. Furthermore, we identify a hitherto undiscussed temporal bias that complicates the simple compar- ison of ancient and reconstructed languages with present-day languages. Due to the sparsity of the data and the absence of statistical and computational methods that can adequately handle this bias, we instead directly target rates of change within and across families, thereby providing a case study to highlight BDPROTO’s research viability; using phylogenetic comparative methods and high-resolution language family trees, we investigate whether consonantal and vocalic systems differ in their rates of change over the last 10,000 years. In light of the compilation of BDPROTO and the findings of our case study, we discuss the challenges involved in comparing the sound systems of reconstructed languages with modern day languages

The potential of automatic word comparison for historical linguistics

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)

Studying Evolutionary Change: Transdisciplinary Advances in Understanding and Measuring Evolution

Evolutionary processes can be found in almost any historical, i.e. evolving, system that erroneously copies from the past. Well studied examples do not only originate in evolutionary biology but also in historical linguistics. Yet an approach that would bind together studies of such evolving systems is still elusive. This thesis is an attempt to narrowing down this gap to some extend. An evolving system can be described using characters that identify their changing features. While the problem of a proper choice of characters is beyond the scope of this thesis and remains in the hands of experts we concern ourselves with some theoretical as well data driven approaches. Having a well chosen set of characters describing a system of different entities such as homologous genes, i.e. genes of same origin in different species, we can build a phylogenetic tree. Consider the special case of gene clusters containing paralogous genes, i.e. genes of same origin within a species usually located closely, such as the well known HOX cluster. These are formed by step- wise duplication of its members, often involving unequal crossing over forming hybrid genes. Gene conversion and possibly other mechanisms of concerted evolution further obfuscate phylogenetic relationships. Hence, it is very difficult or even impossible to disentangle the detailed history of gene duplications in gene clusters. Expanding gene clusters that use unequal crossing over as proposed by Walter Gehring leads to distinctive patterns of genetic distances. We show that this special class of distances helps in extracting phylogenetic information from the data still. Disregarding genome rearrangements, we find that the shortest Hamiltonian path then coincides with the ordering of paralogous genes in a cluster. This observation can be used to detect ancient genomic rearrangements of gene clus- ters and to distinguish gene clusters whose evolution was dominated by unequal crossing over within genes from those that expanded through other mechanisms. While the evolution of DNA or protein sequences is well studied and can be formally described, we find that this does not hold for other systems such as language evolution. This is due to a lack of detectable mechanisms that drive the evolutionary processes in other fields. Hence, it is hard to quantify distances between entities, e.g. languages, and therefore the characters describing them. Starting out with distortions of distances, we first see that poor choices of the distance measure can lead to incorrect phylogenies. Given that phylogenetic inference requires additive metrics we can infer the correct phylogeny from a distance matrix D if there is a monotonic, subadditive function ζ such that ζ^−1(D) is additive. We compute the metric-preserving transformation ζ as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process is missing. Yet, this does not hinder studies of language evolution using automated tools. As the amount of available and large digital corpora increased so did the possibilities to study them automatically. The obvious parallels between historical linguistics and phylogenetics lead to many studies adapting bioinformatics tools to fit linguistics means. Here, we use jAlign to calculate bigram alignments, i.e. an alignment algorithm that operates with regard to adjacency of letters. Its performance is tested in different cognate recognition tasks. Using pairwise alignments one major obstacle is the systematic errors they make such as underestimation of gaps and their misplacement. Applying multiple sequence alignments instead of a pairwise algorithm implicitly includes more evolutionary information and thus can overcome the problem of correct gap placement. They can be seen as a generalization of the string-to-string edit problem to more than two strings. With the steady increase in computational power, exact, dynamic programming solutions have become feasible in practice also for 3- and 4-way alignments. For the pairwise (2-way) case, there is a clear distinction between local and global alignments. As more sequences are consid- ered, this distinction, which can in fact be made independently for both ends of each sequence, gives rise to a rich set of partially local alignment problems. So far these have remained largely unexplored. Thus, a general formal frame- work that gives raise to a classification of partially local alignment problems is introduced. It leads to a generic scheme that guides the principled design of exact dynamic programming solutions for particular partially local alignment problems

Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences

This book is the product of an international workshop dedicated to addressing data accessibility in the linguistics field. It is therefore vital to the book’s mission that its content be open access. Linguistics as a field remains behind many others as far as data management and accessibility strategies. The problem is particularly acute in the subfield of language acquisition, where international linguistic sound files are needed for reference. Linguists' concerns are very much tied to amount of information accumulated by individual researchers over the years that remains fragmented and inaccessible to the larger community. These concerns are shared by other fields, but linguistics to date has seen few efforts at addressing them. This collection, undertaken by a range of leading experts in the field, represents a big step forward. Its international scope and interdisciplinary combination of scholars/librarians/data consultants will provide an important contribution to the field