Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System
Pascual Juli\'an-Iranzo, Fernando S\'aenz-P\'erez
TL;DR
This paper presents methods to incorporate WordNet-based lexical similarity measures into a Fuzzy Logic Programming system, enabling more flexible lexical reasoning and enhancing natural language processing capabilities.
Contribution
It introduces techniques for integrating WordNet similarity measures and proximity equations into a Fuzzy Logic Programming system, expanding its lexical reasoning abilities.
Findings
Implemented standard and new similarity measures for WordNet
Enabled proximity equations with approximation degrees
Enhanced natural language processing applications
Abstract
This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing applications. [Under consideration in Theory and Practice of Logic Programming (TPLP)]
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