Non-randomness in Morphological Diversity: A Computational Approach Based on Multilingual Corpora


The project applies information theory, statistical modelling and machine learning to the study of language adaptation using linguistic data extracted from multilingual corpora. In addition to the theoretical findings, the project will provide a data set consisting of text samples of 100 languages facilitating future use of corpus-based computational methods in scientific approaches to linguistic diversity and change.

Project members:  Olga Sozinova (PhD student), Christian Bentz (PostDoc, external collaborator), Ximena Gutierrez-Vasques (PostDoc, Swiss Government Excellence Scholarship), Steven Moran (PostDoc), and Tanja Samardžić (PI).

Funding:  SNF grant #176305 2018—2022. 

100-language sample

Data for the analysis consists of texts in 100 languages, which will be published as a multilingual corpus. The chosen 100-language sample is proposed by WALS. The text collection for this sample of languages will be our original contribution.

Current collection

Current number of tokens collected per genre: fiction, non-fiction, conversation, professional, technical, grammar examples.

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