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

Laura Franzoi | Zendy; Andrea Sgarro | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Laura Franzoi,

Andrea Sgarro

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.163

Subject(s) - fuzzy logic , hamming distance , computer science , frame (networking) , class (philosophy) , linguistics , probabilistic logic , axiom , logical connective , t norm , hamming code , fuzzy classification , artificial intelligence , mathematics , algebra over a field , algorithm , fuzzy set , pure mathematics , philosophy , geometry , telecommunications , decoding methods , block code

Back in 1967 the linguist Z. 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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research