# A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty

arXiv: https://arxiv.org/abs/1710.08327 · HTML: https://mlsanity.com/paper/1710.08327/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2017.

## Built on by (most cited first, 6 of 6)

- [RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification](https://mlsanity.com/paper/2110.15426/) (2021)
- [Investigating Disagreement in the Scientific Literature](https://mlsanity.com/paper/2107.14641/) (2021)
- [Annotating Scientific Uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches](https://mlsanity.com/paper/2503.11376/) (2025)
- [UnScientify: Detecting Scientific Uncertainty in Scholarly Full Text](https://mlsanity.com/paper/2307.14236/) (2023)
- [Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection](https://mlsanity.com/paper/2305.04181/) (2023)
- [GAPMAP: Mapping Scientific Knowledge Gaps in Biomedical Literature Using Large Language Models](https://mlsanity.com/paper/2510.25055/) (2025)

## Abstract (arXiv)

Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists' domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertain types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts' unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words are verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.