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

这项工作解决了有关语义相关性（SR）措施的关键开放问题，这是一系列用于整个计算机科学和相关领域的算法，有助于计算机复制人类对两个概念之间相关性的评估。几十年的研究和开发已经将SR措施转变为从信息检索到人机交互再到空间计算等领域的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，研究人员最近才开始从以人为本的角度来研究它。这些以人为中心的研究使整个SR文献的关键假设问题化，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值。该项目解决了SR文献中长期存在的开放性问题，这将使该领域朝着更加以人为本的SR方法发展。为此，本研究将收集相关性判断的数据集，挖掘维基百科和其他内容中的模式，进行统计分析，创建和评估算法，开发软件，并进行大规模的用户研究。这项研究将完成四个工作线程，重新定义语义相关性，以解决SR文献中提出的以人为中心的问题：（1）研究文化在社会责任中的作用，并利用这些结果重新定义社会责任，以纳入文化背景，（2）在对最终用户至关重要但完全被SR文献忽略的低关注度概念中研究SR，（3）解决SR措施的需求，向用户解释其相关性估计，（4）开发更强大的以人为本的SR评估程序，并通过易于使用的软件支持其采用。其次，这项研究将开发新的概念表示SR措施，以适应不同的文化观点，并创建紧凑的上下文SR模型，使应用程序具有易于处理的以人为中心的SR算法。最后，本研究将展示以人为本的SR方法的力量，通过它们在改进的推荐系统，增强维基百科的读者体验，和新的信息发现工具中的应用。

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.