Language Contact and Borrowing The most fascinating thing about language is that it is one of the biggest and fastest changing systems in humanity. Language change is not only driven by internal changes of sounds, words, or meanings, but also by language contact and the borrowing of words. Language diversification is represented in the tree model. However, it is well-known in historical linguistics that the tree model of language diversification does not fully capture reality. In order to model all aspects of language evolution, the network model seems to be the solution. While the underlying tree represents the diversification of the languages, reticulations can be added to display language contact and lexical flow.
During language contact, words can be exchanged between the languages. The diving factors of borrowing, as well as the driving force of speakers to borrowed words is contact depended. The missing universal principles to borrow words and the unique adaptation process of the words in the recipient language, makes the identification of loanwords a difficult task. Historical linguistics develop methods to compare languages, reconstruct ancestral states, and identify sound changes. The comparative method can also shed light on language contact and loanwords.
The aim of my work is to identify contact scenarios and borrowing processes between languages in order to shed light on the relationship and evolution of languages. The main challenge in linguistics is to collect gold-standard data and identify contact situation.
Computational models Mathematical models and computational tools, developed in phylogenetics, population genetics, and informatics can be adapted into historical linguistics to study linguistic prehistory. Several computational methods (e.g. automatic tree reconstruction and cognate detection) can be considered as state-of-the-art methods to reconstruct language evolution and diversity. Models for loanword detection and contact inference are still in their infancy. The main obstacle for applying computational models to the study of language contact is the relative sparseness of language data and gold standard data. Next to my work of pursuing the collection of gold-standard data, is the adaptation of computational models into historical linguistics to automize the processes of loanword and contact identification. Next to methods from phylogenetics and population genetics, mathematical methods like Bayesian models, machine learning, and deep learning can be adopted for this purpose.