Dataset Information

Using lexical language models to detect borrowings in monolingual wordlists.

ABSTRACT: 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.

SUBMITTER: Miller JE

PROVIDER: S-EPMC7725347 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using lexical language models to detect borrowings in monolingual wordlists.

Miller John E JE Tresoldi Tiago T Zariquiey Roberto R Beltrán Castañón César A CA Morozova Natalia N List Johann-Mattis JM

PloS one 20201209 12

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 expe ...[more]

PMID: 33296372

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Project description:Background: The language profiles of children with language impairment (LI) and bilingual children can show partial, and possibly temporary, overlap. The current study examined the persistence of this overlap over time. Furthermore, we aimed to better understand why the language profiles of these two groups show resemblance, testing the hypothesis that the language difficulties of children with LI reflect a weakened ability to maintain attention to the stream of linguistic information. Consequent incomplete processing of language input may lead to delays that are similar to those originating from reductions in input frequency. Methods: Monolingual and bilingual children with and without LI (N = 128), aged 5-8 years old, participated in this study. Dutch receptive vocabulary and grammatical morphology were assessed at three waves. In addition, auditory and visual sustained attention were tested at wave 1. Mediation analyses were performed to examine relationships between LI, sustained attention, and language skills. Results: Children with LI and bilingual children were outperformed by their typically developing (TD) and monolingual peers, respectively, on vocabulary and morphology at all three waves. The vocabulary difference between monolinguals and bilinguals decreased over time. In addition, children with LI had weaker auditory and visual sustained attention skills relative to TD children, while no differences between monolinguals and bilinguals emerged. Auditory sustained attention mediated the effect of LI on vocabulary and morphology in both the monolingual and bilingual groups of children. Visual sustained attention only acted as a mediator in the bilingual group. Conclusion: The findings from the present study indicate that the overlap between the language profiles of children with LI and bilingual children is particularly large for vocabulary in early (pre)school years and reduces over time. Results furthermore suggest that the overlap may be explained by the weakened ability of children with LI to sustain their attention to auditory stimuli, interfering with how well incoming language is processed.

Dataset Information

Using lexical language models to detect borrowings in monolingual wordlists.

Publications

Using lexical language models to detect borrowings in monolingual wordlists.

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