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

arXiv: https://arxiv.org/abs/2309.06132 · HTML: https://mlsanity.com/paper/2309.06132/

## 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.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Automatic Detection of Vague Words and Sentences in Privacy Policies](https://mlsanity.com/paper/1808.06219/) (2018)
- [FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection](https://mlsanity.com/paper/2104.04828/) (2021)
- [Combining Vagueness Detection with Deep Learning to Identify Fake News](https://mlsanity.com/paper/2110.14780/) (2021)
- [Modeling Language Vagueness in Privacy Policies using Deep Neural Networks](https://mlsanity.com/paper/1805.10393/) (2018)

## Built on by (most cited first, 4 of 4)

- [Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification](https://mlsanity.com/paper/2402.03780/) (2024)
- [HYBRINFOX at CheckThat! 2024 -- Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection](https://mlsanity.com/paper/2407.03770/) (2024)
- [Fairness Interventions in Classification: A Study on AI Explainability](https://mlsanity.com/paper/2407.14766/) (2024)
- [Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign](https://mlsanity.com/paper/2608.15746/) (2026)

## 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.