Clustering and Relational Ambiguity: from Text Data to Natural Data

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, main...

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Bibliographic Details
Main Author: Nicolas Turenne
Format: Article
Language:English
Published: Nicolas Turenne 2014-06-01
Series:Journal of Data Mining and Digital Humanities
Subjects:
Online Access:https://jdmdh.episciences.org/13/pdf
Description
Summary: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.
ISSN:2416-5999