SLAM : Solutions lexicales automatique pour m\'etaphores

TL;DR

SLAM is an automatic system that identifies conventional solutions to lexical metaphors by intersecting semantic axes derived from a synonym network and a corpus, advancing metaphor understanding.

Contribution

It introduces SLAM, a novel method combining paradigmatic and syntagmatic axes to solve lexical metaphors automatically.

Findings

Successfully applied on DicoSyn and Frantext corpus

Achieved accurate identification of conventional metaphor solutions

Demonstrated potential for computational metaphor analysis

Abstract

This article presents SLAM, an Automatic Solver for Lexical Metaphors like ?d\'eshabiller* une pomme? (to undress* an apple). SLAM calculates a conventional solution for these productions. To carry on it, SLAM has to intersect the paradigmatic axis of the metaphorical verb ?d\'eshabiller*?, where ?peler? (?to peel?) comes closer, with a syntagmatic axis that comes from a corpus where ?peler une pomme? (to peel an apple) is semantically and syntactically regular. We test this model on DicoSyn, which is a ?small world? network of synonyms, to compute the paradigmatic axis and on Frantext.20, a French corpus, to compute the syntagmatic axis. Further, we evaluate the model with a sample of an experimental corpus of the database of Flexsem