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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.
Experiments with a readily available system 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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