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

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dc.creatorNock, Richard
dc.creatorVaillant, Pascal
dc.creatorNielsen, Frank
dc.creatorHenry, Claudia
dc.date2008-10-07
dc.date.accessioned2026-07-07T10:08:11Z
dc.date.available2026-07-07T10:08:11Z
dc.descriptionWithout 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.
dc.description6 pages, 7 embedded figures, LaTeX 2e using the ecai2006.cls document class and the algorithm2e.sty style file (+ standard packages like epsfig, amsmath, amssymb, amsfonts...). Extends the short version contained in the ECAI 2006 proceedings
dc.identifierhttps://arxiv.org/abs/0810.1261
dc.identifierhttp://arxiv.org/abs/0810.1261
dc.identifierECAI 2006: 17th European Conference on Artificial Intelligence. Riva del Garda, Italy, 29 August - 1st September 2006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170904
dc.subjectComputation and Language
dc.subjectInformation Retrieval
dc.subjectH.3.3; I.2.7
dc.titleSoft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm
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