论文标题
PEOPLEMAP:可视化工具,用于使用自然语言处理绘制研究人员
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
论文作者
论文摘要
在机构中发现研究专业知识可能是一项艰巨的任务。手动策划的大学目录很容易变得过时,他们常常缺乏了解研究人员的利益和过去工作所需的信息,因此很难探索机构的研究多样性并确定研究才能。这导致内部和外部实体发现新的联系并培养研究合作的机会失去了机会。为了解决这个问题,我们开发了PeopleMap,这是第一个互动,开源的,基于Web的工具,它通过利用自然语言处理(NLP)技术产生的嵌入来根据其研究兴趣和出版物来视觉上“映射”研究人员。 PeopleMap为机构提供了一种新的引人入胜的方式,可以总结其研究才能和人们发现新的联系。该平台以易于使用和可持续性开发。仅将研究人员的Google Scholar概况作为投入,任何机构都可以使用其公开访问的存储库和详细文档来轻松地采用PeopleMap。
Discovering research expertise at institutions can be a difficult task. Manually curated university directories easily become out of date and they often lack the information necessary for understanding a researcher's interests and past work, making it harder to explore the diversity of research at an institution and identify research talents. This results in lost opportunities for both internal and external entities to discover new connections and nurture research collaboration. To solve this problem, we have developed PeopleMap, the first interactive, open-source, web-based tool that visually "maps out" researchers based on their research interests and publications by leveraging embeddings generated by natural language processing (NLP) techniques. PeopleMap provides a new engaging way for institutions to summarize their research talents and for people to discover new connections. The platform is developed with ease-of-use and sustainability in mind. Using only researchers' Google Scholar profiles as input, PeopleMap can be readily adopted by any institution using its publicly-accessible repository and detailed documentation.