UnScientify: Detecting Scientific Uncertainty in Scholarly Full Text
Panggih Kusuma Ningrum Philipp Mayr Iana Atanassova
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.