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Item type:Publication, Using lexical language models to detect borrowings in monolingual wordlists(Public Library of Science, 2020-12-01)Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational approaches in comparative linguistics, automated approaches to lexical borrowing detection are still in their infancy, disregarding many aspects of the evidence that is routinely considered by human experts. One example for this kind of evidence are phonological and phonotactic clues that are especially useful for the detection of recent borrowings that have not yet been adapted to the structure of their recipient languages. In this study, we test how these clues can be exploited in automated frameworks for borrowing detection. By modeling phonology and phonotactics with the support of Support Vector Machines, Markov models, and recurrent neural networks, we propose a framework for the supervised detection of borrowings in mono-lingual wordlists. Based on a substantially revised dataset in which lexical borrowings have been thoroughly annotated for 41 different languages from different families, featuring a large typological diversity, we use these models to conduct a series of experiments to investigate their performance in mono-lingual borrowing detection. While the general results appear largely unsatisfying at a first glance, further tests show that the performance of our models improves with increasing amounts of attested borrowings and in those cases where most borrowings were introduced by one donor language alone. Our results show that phonological and phonotactic clues derived from monolingual language data alone are often not sufficient to detect borrowings when using them in isolation. Based on our detailed findings, however, we express hope that they could prove to be useful in integrated approaches that take multi-lingual information into account. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring Amazonian Cognitive Diversity at Chana Research Station(Wiley, 2026-01-01)The Chana Research Station for Language Sciences and Interculturality is a scientific initiative dedicated to exploring the linguistic, cultural, and cognitive diversity of the Peruvian Amazon. Building on long-standing local partnerships and collaborations, Chana facilitates research on cognition that extends beyond populations from industrialized democracies, traditionally overrepresented in behavioral sciences. Other populations-such as those in Amazonia-exhibit remarkable diversity. With this in mind, current Chana projects and collaborations encompass research on how Amazonian unique linguistic structures and culturally significant practices shape language processing and acquisition, as well as nonlinguistic cognition; studies on cognition focused on quantification practices in cultures with limited or unconventional numeral systems; and cross-cultural investigations of perception, building on prior research in other regions of the Amazon and the world. Beyond human cognition, the station has also supported experimental research on the relationship between domesticated dogs and their owners. Chana prioritizes inclusive and ethical research that benefits and includes local communities and their perspectives by engaging local and Indigenous researchers, and leading social and educational projects focused on cultural revitalization. Chana welcomes collaboration with researchers who are interested in advancing the understanding of Amazonian societies, languages, and knowledge systems.