CHS: Small: Collaborative Research: Human-Centered Semantic Relatedness

这项工作解决了关于语义相关性(SR)度量的关键悬而未决的问题，语义相关性度量是计算机科学和相关领域中使用的一系列算法，帮助计算机复制人类对两个概念之间相关性的评估。经过数十年的研究和开发，SR测量已成为从信息检索到人机交互再到空间计算等领域广泛智能技术的关键组成部分。然而，尽管SR的重要性和普遍性，研究人员直到最近才开始从以人为中心的角度来研究它。这些以人为中心的研究使整个SR文献背后的关键假设产生了问题，例如，来自所有文化背景的人都同意任何两个概念之间的单一关联性价值。这个项目解决了SR文献中期待已久的开放问题，这些问题将使该领域走向更以人为中心的SR方法。首先，这项研究将完成四个重新定义语义相关性的工作，以解决SR文献中提出的以人为中心的问题：(1)调查文化在SR中的作用，并使用这些结果重新定义SR以纳入文化语境；(2)在对终端用户至关重要但被SR文献完全忽略的低知名度概念中，研究SR，(3)解决对SR措施的需求，以向用户解释其相关性估计，以及(4)开发更健壮的以人为中心的SR评估程序，并通过易于使用的软件支持其采用。其次，这项研究将为SR度量开发新的概念表示，以适应不同的文化视角，并创建紧凑的上下文SR模型，使应用程序能够使用易于处理的以人为中心的SR算法。最后，这项研究将通过在改进的推荐系统、增强的维基百科读者体验和新颖的信息发现工具中的应用来展示以人为中心的SR方法的力量。

This work addresses key open questions about semantic relatedness (SR) measures, a family of algorithms used throughout computer science and related fields that help computers replicate human assessments of the relatedness between two concepts. Decades of research and development have transformed SR measures into a critical component of a wide swath of intelligent technologies in areas ranging from information retrieval to human-computer interaction to spatial computing. However, despite the importance and ubiquity of SR, researchers have only recently begun to examine it from a human-centered perspective. These human-centered studies have problematized key assumptions underlying the entire SR literature, e.g. that people from all cultural contexts agree on a single relatedness value between any two concepts. This project addresses long-overdue open questions in the SR literature that will move the field towards a more human-centered approach to SR. To do so, this research will collect datasets of relatedness judgments, mine patterns in Wikipedia and other content, perform statistical analyses, create and evaluate algorithms, develop software, and conduct large-scale user studies.First, this research will complete four threads of work that redefine semantic relatedness to address human-centered concerns raised in the SR literature: (1) investigate the role of culture in SR and use these results to redefine SR to incorporate cultural context, (2) study SR among the low-notability concepts that are critical to end users but entirely ignored by the SR literature, (3) address the need for SR measures that explain their relatedness estimates to users, and (4) develop more robust human-centered SR evaluation procedures and support their adoption through easy-to-use software. Second, this research will develop new conceptual representations for SR measures that accommodate differing cultural perspectives and create compact contextual SR models that empower applications with tractable human-centered SR algorithms. Finally, the research will demonstrate the power of human-centered SR approaches through their application in improved recommender systems, enhanced Wikipedia reader experiences, and novel information discovery tools.