# UnScientify: Detecting Scientific Uncertainty in Scholarly Full Text

arXiv: https://arxiv.org/abs/2307.14236 · HTML: https://mlsanity.com/paper/2307.14236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty](https://mlsanity.com/paper/1710.08327/) (2017)

## Built on by (most cited first, 1 of 1)

- [Annotating Scientific Uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches](https://mlsanity.com/paper/2503.11376/) (2025)

## Abstract (arXiv)

This demo paper presents UnScientify, an interactive system designed to detect scientific uncertainty in scholarly full text. The system utilizes a weakly supervised technique that employs a fine-grained annotation scheme to identify verbally formulated uncertainty at the sentence level in scientific texts. The pipeline for the system includes a combination of pattern matching, complex sentence checking, and authorial reference checking. Our approach automates labeling and annotation tasks for scientific uncertainty identification, taking into account different types of scientific uncertainty, that can serve various applications such as information retrieval, text mining, and scholarly document processing. Additionally, UnScientify provides interpretable results, aiding in the comprehension of identified instances of scientific uncertainty in text.