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

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