标题： 当生命路径交汇时：从维基百科中提取时间和空间中的人类交互 显示英文标题

标题： When Life Paths Cross: Extracting Human Interactions in Time and Space from Wikipedia

摘要： 在著名人物之间的互动——无论是在个体、群体还是网络中进行研究——往往在文化、经济、政治、科学和历史视角下传达重要的信息。 通过分析这些互动的时间和地点，我们可以观察到不同地区随时间推移的动态变化。 然而，相关研究常常受到数据稀缺的限制，尤其是在具体地点和时间信息的可用性方面。 为了解决这个问题，我们从维基百科挖掘了数百万条传记页面，提取了以（Person1，Person2，Time，Location）形式的685,966条互动四元组记录。 这些互动的关键元素通常分散在异构的众包文本中，可能与互动松散或间接相关。 我们通过设计一种结合注意力机制、多任务学习和特征迁移方法的模型，克服了这一挑战，取得了86.51%的F1分数，这优于基线模型。 我们进一步对政治人物内部和跨党派的互动进行了实证分析，以研究美国的政治极化现象，展示了从这种数据中获得的视角可能带来的潜力。 我们公开提供代码、提取的互动数据以及包含4,507个标记互动四元组的WikiInteraction数据集。 显示英文摘要

摘要： Interactions among notable individuals -- whether examined individually, in groups, or as networks -- often convey significant messages across cultural, economic, political, scientific, and historical perspectives. By analyzing the times and locations of these interactions, we can observe how dynamics unfold across regions over time. However, relevant studies are often constrained by data scarcity, particularly concerning the availability of specific location and time information. To address this issue, we mine millions of biography pages from Wikipedia, extracting 685,966 interaction records in the form of (Person1, Person2, Time, Location) interaction quadruplets. The key elements of these interactions are often scattered throughout the heterogeneous crowd-sourced text and may be loosely or indirectly associated. We overcome this challenge by designing a model that integrates attention mechanisms, multi-task learning, and feature transfer methods, achieving an F1 score of 86.51%, which outperforms baseline models. We further conduct an empirical analysis of intra- and inter-party interactions among political figures to examine political polarization in the US, showcasing the potential of the extracted data from a perspective that may not be possible without this data. We make our code, the extracted interaction data, and the WikiInteraction dataset of 4,507 labeled interaction quadruplets publicly available.