论文标题
Ultra：一种以数据驱动的方式推荐团队成立，以响应提案电话
ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls
论文作者
论文摘要
当研究人员应对资助机构提出建议的呼吁时，我们介绍了一种新兴的基于AI的方法和原型系统，以协助团队形成。当需求机会定期出现时，这是建立团队的总体问题的一个实例，并且潜在的成员可能会随着时间的流逝而有所不同。我们方法的新颖性是：（a）提取有关研究人员所需的技术技能以及来自多个数据源的呼叫，并使用自然语言处理（NLP）技术将其归一化，（b）基于基于约束的匹配和团队来构建原型解决方案，（c）描述来自大学研究人员的初步反馈，以使用和（d）来创建和发布其他数据，并使用其他数据来使用。
We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple data sources and normalize them using Natural Language Processing (NLP) techniques, (b) build a prototype solution based on matching and teaming based on constraints, (c) describe initial feedback about system from researchers at a University to deploy, and (d) create and publish a dataset that others can use.
