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.

翻译：本演示论文介绍UnScientify，一个用于检测学术全文中科学不确定性的交互式系统。该系统采用弱监督技术，通过细粒度标注方案在科学文本中识别以语言形式表达的句子级不确定性。系统流程包括模式匹配、复杂句检测及作者引用核查的组合方法。我们的方法自动化了科学不确定性识别中的标注任务，涵盖不同类型的科学不确定性，可服务于信息检索、文本挖掘及学术文档处理等多种应用场景。此外，UnScientify提供可解释结果，有助于理解文本中已识别的科学不确定性实例。