Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO

Benjamin Icard (1) , Vincent Claveau (2) , Ghislain Atemezing (3) , Paul Égré (1)

1 IJN - Institut Jean-Nicod
2 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
3 Mondeca

We present a hybrid approach to the automated measurement of vagueness and subjectivity in texts. We first introduce the expert system VAGO, we illustrate it on a small benchmark of fact vs. opinion sentences, and then test it on the larger French press corpus FreSaDa to confirm the higher prevalence of subjective markers in satirical vs. regular texts. We then build a neural clone of VAGO, based on a BERT-like architecture, trained on the symbolic VAGO scores obtained on FreSaDa. Using explainability tools (LIME), we show the interest of this neural version for the enrichment of the lexicons of the symbolic version, and for the production of versions in other languages.

Mots clés: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); FOS: Computer and information sciences; 68T07; 68T50

Domaines: Linguistique; Informatique et langage [cs.CL]

Benjamin Icard, Vincent Claveau, Ghislain Atemezing, Paul Égré. Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO. 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Oct 2023, Venise, Italy. ⟨hal-04363822⟩