mlsanityData to 14 Oct 2026

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

arXiv 2309.06132 ↗First posted 12 Sep 2023cs.CL cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 12 Sep 2023.

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8 of 28 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2019CamemBERT: a Tasty French Language Modelcited by 368
  5. 2018Automatic Detection of Vague Words and Sentences in Privacy Policiescited by 10
  6. 2021FreSaDa: A French Satire Data Set for Cross-Domain Satire Detectioncited by 8
  7. 2021Combining Vagueness Detection with Deep Learning to Identify Fake Newscited by 6
  8. 2018Modeling Language Vagueness in Privacy Policies using Deep Neural Networkscited by 5

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Citing papers per year: 2024: 3; 2025: 0; 2026: 1.

4 papers.