Clustering and Relational Ambiguity: from Text Data to Natural Data
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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.
2021 ◽
Vol 9
(09)
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pp. 484-488
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1976 ◽
Vol 66
(4)
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pp. 1413-1424
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1978 ◽
Vol 36
(2)
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pp. 32-33
1987 ◽
Vol 45
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pp. 642-643
1990 ◽
Vol 48
(2)
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pp. 374-375
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1996 ◽
Vol 54
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pp. 680-681
1989 ◽
Vol 47
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pp. 552-553
1993 ◽
Vol 51
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pp. 240-241
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