# VIANA: Visual Interactive Annotation of Argumentation

arXiv: https://arxiv.org/abs/1907.12413 · HTML: https://mlsanity.com/paper/1907.12413/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)
- [On the Visualisation of Argumentation Graphs to Support Text Interpretation](https://mlsanity.com/paper/2303.03235/) (2023)
- [BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis](https://mlsanity.com/paper/2308.08003/) (2023)
- [d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement](https://mlsanity.com/paper/2507.11960/) (2025)
- [Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment](https://mlsanity.com/paper/2609.26338/) (2026)
- [ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts](https://mlsanity.com/paper/2609.27014/) (2026)

## Abstract (arXiv)

Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on time-consuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a long-term collaboration with domain experts, we identify and model five high-level analysis tasks. We enable close reading and note-taking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text- and graph-based layers. We evaluate our system with a two-stage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.