Title: A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty,
Abstract: 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.