UnScientify：检测学术全文中的科学不确定性
UnScientify: Detecting Scientific Uncertainty in Scholarly Full Text
AI总结：
UnScientify是一个交互式系统，采用弱监督和细粒度标注方案，结合模式匹配等方法，自动检测学术全文中的科学不确定性，并提供可解释结果以支持多种文本处理应用。
摘要：
本演示论文介绍了UnScientify，一个用于检测学术全文中的科学不确定性的交互式系统。该系统采用弱监督技术，利用细粒度标注方案在句子层面识别科学文本中以语言形式表达的不确定性。系统流程包括模式匹配、复杂句子检查和作者引用检查的组合。我们的方法自动化了科学不确定性识别的标注和注释任务，考虑了不同类型的科学不确定性，可服务于信息检索、文本挖掘和学术文档处理等多种应用。此外，UnScientify提供可解释的结果，有助于理解文本中已识别的科学不确定性实例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
