Scientific jargon can impede researchers when they read materials from other
domains. Current methods of jargon identification mainly use corpus-level
familiarity indicators (e.g., Simple Wikipedia represents plain language).
However, researchers' familiarity of a term can vary greatly based on their own
background. We collect a dataset of over 10K term familiarity annotations from
11 computer science researchers for terms drawn from 100 paper abstracts.
Analysis of this data reveals that jargon familiarity and information needs
vary widely across annotators, even within the same sub-domain (e.g., NLP). We
investigate features representing individual, sub-domain, and domain knowledge
to predict individual jargon familiarity. We compare supervised and
prompt-based approaches, finding that prompt-based methods including personal
publications yields the highest accuracy, though zero-shot prompting provides a
strong baseline. This research offers insight into features and methods to
integrate personal data into scientific jargon identification.

科学术语识别中，熟悉度指标的使用存在问题。本研究通过收集 11000 多个计算机科学研究人员对术语的熟悉度进行注释分析，发现不同研究人员对术语的熟悉度和信息需求存在巨大差异。研究探讨了个人、子领域和领域知识等特征，以预测个体对术语的熟悉度。结果表明，以个人出版物为基础的提示方法达到了最高的准确率，而零样本提示方法提供了强有力的基准。本研究提供了将个人数据整合到科学术语识别中的特征和方法的洞见。