Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO
This work addresses the problem of analyzing text vagueness and subjectivity for researchers in NLP, but it is incremental as it builds on existing symbolic methods with neural enhancements.
The paper tackles the automated measurement of vagueness and subjectivity in texts by introducing a hybrid approach that combines a symbolic expert system (VAGO) with a neural clone based on BERT, showing its application on datasets like FreSaDa to confirm higher subjective markers in satirical vs. regular texts.
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.