  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.
