Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature
Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature
复制标题
图书馆员在环：一种用于检测学术文献中研究数据的非正式提及的自然语言处理范例
DOI：
10.48550/arxiv.2203.05112
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Libby Hemphill
中科院分区：
文献类型：
--
作者：
Lizhou Fan;Sara Lafia;David A. Bleckley;E. Moss;A. Thomer;Libby Hemphill
数据引用为研究数据影响提供了基础。数据引文的收集和管理是档案学和学术交流的新领域。然而，研究数据引用的发现和管理是劳动密集型的。引用唯一标识符（即DOI）的数据引用很容易找到;然而，对研究数据的非正式提及更难推断。我们提出了一个自然语言处理（NLP）的范例，以支持人类的任务，确定非正式提到的研究数据集。发现非正式数据提及的工作目前由跨大学政治和社会研究联盟（ICPSR）的图书馆员及其工作人员执行，这是一个大型社会科学数据档案馆，维护着大量与数据相关的文献书目。NLP模型是从ICPSR的图书馆员积极收集的数据引用中引导出来的。该模型将模式匹配与人工注释的多次迭代相结合，以学习用于检测非正式数据提及的其他规则。然后，这些示例用于训练NLP管道。图书馆员在循环范式以ICPSR图书馆员执行的数据工作为中心，支持更广泛的努力，以建立更全面的数据相关文献书目，反映研究数据用户的学术社区。
Data citations provide a foundation for studying research data impact. Collecting and managing data citations is a new frontier in archival science and scholarly communication. However, the discovery and curation of research data citations is labor intensive. Data citations that reference unique identifiers (i.e. DOIs) are readily findable; however, informal mentions made to research data are more challenging to infer. We propose a natural language processing (NLP) paradigm to support the human task of identifying informal mentions made to research datasets. The work of discovering informal data mentions is currently performed by librarians and their staff in the Inter-university Consortium for Political and Social Research (ICPSR), a large social science data archive that maintains a large bibliography of data-related literature. The NLP model is bootstrapped from data citations actively collected by librarians at ICPSR. The model combines pattern matching with multiple iterations of human annotations to learn additional rules for detecting informal data mentions. These examples are then used to train an NLP pipeline. The librarian-in-the-loop paradigm is centered in the data work performed by ICPSR librarians, supporting broader efforts to build a more comprehensive bibliography of data-related literature that reflects the scholarly communities of research data users.
DOI：
10.1002/asi.24646
发表时间：
2022-10
影响因子：
3.5
作者：
Hemphill, Libby;Pienta, Amy;Lafia, Sara;Akmon, Dharma;Bleckley, David A.
通讯作者：
Bleckley, David A.