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Procedia Computer Science
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Procedia Computer Science
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License: CC BY NC ND
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Linguistic classification: T-norms, fuzzy distances and fuzzy distinguishabilities

Authors: Laura Franzoi; Andrea Sgarro;

Linguistic classification: T-norms, fuzzy distances and fuzzy distinguishabilities

Abstract

Abstract Back in 1967 the linguist Ž. Muljacic used an additive distance between ill-defined linguistic features which is a forerunner of the fuzzy Hamming distance between strings of truth values in standard fuzzy logic. Here we show that if the logical frame is changed one obtains additive distances which are either sorely inadequate, as in the Łukasiewicz or probabilistic case, or coincide with the distance originally envisaged by Muljacic, as happens with a whole class of T-norms (abstract logical conjunctions) which includes the nilpotent minimum. All this strengthens the role of Muljacic distances in linguistic clustering and of Muljacic distinguishabilities (a notion subtly different from distances, but quite inalienable) in linguistic evolution. As a preliminary example we re-take and re-examine Muljacic original data.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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5
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