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arXiv · 0810.1261

Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm

Richard Nock·Pascal Vaillant·Frank Nielsen·Claudia Henry
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Abstract

Without prior knowledge, distinguishing different languages may be a hard task, especially when their borders are permeable. We develop an extension of spectral clustering -- a powerful unsupervised classification toolbox -- that is shown to resolve accurately the task of soft language distinction. At the heart of our approach, we replace the usual hard membership assignment of spectral clustering by a soft, probabilistic assignment, which also presents the advantage to bypass a well-known complexity bottleneck of the method. Furthermore, our approach relies on a novel, convenient construction of a Markov chain out of a corpus. Extensive experiments with a readily available system clearly display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.

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Richard Nock, Pascal Vaillant, Frank Nielsen, Claudia Henry. 2008-10-07. Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm. https://arxiv.org/abs/0810.1261

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