UnScientify: Detecting Scientific Uncertainty in Scholarly Full Text
This work addresses the need for automated uncertainty detection in scientific texts, which is useful for researchers and scholars in information retrieval and document processing, though it appears incremental as it builds on existing annotation and pattern-based methods.
The authors tackled the problem of detecting scientific uncertainty in scholarly full text by developing UnScientify, an interactive system that uses a weakly supervised technique with pattern matching and other checks to identify uncertainty at the sentence level, achieving automated labeling for applications like information retrieval and text mining.
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.