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arXiv · 1311.5401

Clustering and Relational Ambiguity: from Text Data to Natural Data

Nicolas Turenne
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Abstract

Text data is often seen as "take-away" materials with little noise and easy to process information. Main questions are how to get data and transform them into a good document format. But data can be sensitive to noise oftenly called ambiguities. Ambiguities are aware from a long time, mainly because polysemy is obvious in language and context is required to remove uncertainty. I claim in this paper that syntactic context is not suffisant to improve interpretation. In this paper I try to explain that firstly noise can come from natural data themselves, even involving high technology, secondly texts, seen as verified but meaningless, can spoil content of a corpus; it may lead to contradictions and background noise.

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Nicolas Turenne. 2013-11-21. Clustering and Relational Ambiguity: from Text Data to Natural Data. https://doi.org/10.46298/jdmdh.4

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