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Revitalizing Endangered Languages

Edited by Justyna Olko & Julia Sallabank

Revitalizing Endangered Languages "This guidebook provides ideas and strategies, as well as some background, to help with the effective revitalization of endangered languages. It covers a broad scope of themes including effective planning, benefits, wellbeing, economic aspects, attitudes and ideologies."


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Academic Paper


Title: Machine learning theory and practice as a source of insightinto universal grammar
Author: Shalom Lappin
Institution: King's College London
Author: Stuart M. Shieber
Institution: Harvard University
Linguistic Field: Computational Linguistics
Abstract: In this paper, we explore the possibility that machine learning approaches to natural-language processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.

CUP AT LINGUIST

This article appears IN Journal of Linguistics Vol. 43, Issue 2.

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