A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions | Zendy

Wataru Okamoto | Zendy; Shun’ichi Tano | Zendy; Atsushi Inoue | Zendy; Ryosuke Fujioka | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions

Author(s) -

Wataru Okamoto,

Shun’ichi Tano,

Atsushi Inoue,

Ryosuke Fujioka

Publication year - 2007

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2007.p0502

Subject(s) - computer science , predicate (mathematical logic) , fuzzy logic , natural language , type (biology) , inference , artificial intelligence , natural language processing , algorithm , programming language , ecology , biology

We propose a generalized inference method for constructing natural language communication. The method is used to obtain fuzzy quantifier Q' when “QA are F isτ ⇒ Q' (m'A) are mF is m''τ” is inferred (Q, Q': fuzzy quantifiers, A: fuzzy subject, m, m', m": modifiers, F: fuzzy predicate, τ: truth qualifier). We show that Q’ is resolved step by step for a non-increasing type (few,...) and a non-decreasing type (most,...).

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