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Mining Frequent Patterns from Human Interactions in Meetings Using Directed Acyclic Graphs

机译：使用指示的无循环图，在会议中挖掘人类交互的频繁模式

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In modern life, interactions between human beings frequently occur in meetings, where topics are discussed. Semantic knowledge of meetings can be revealed by discovering interaction patterns from these meetings. An existing method mines interaction patterns from meetings using tree structures. However, such a tree-based method may not capture all kinds of triggering relations between interactions, and it may not distinguish a participant of a certain rank from another participant of a different rank in a meeting. Hence, the tree-based method may not be able to find all interaction patterns such as those about correlated interaction. In this paper, we propose to mine interaction patterns from meetings using an alternative data structure—namely, a directed acyclic graph (DAG). Specifically, a DAG captures both temporal and triggering relations between interactions in meetings. Moreover, to distinguish one participant of a certain rank from another, we assign weights to nodes in the DAG. As such, a meeting can be modeled as a weighted DAG, from which weighted frequent interaction patterns can be discovered. Experimental results showed the effectiveness of our proposed DAG-based method for mining interaction patterns from meetings.
机译：在现代生活中，人类之间的相互作用经常发生在会议中，其中讨论主题。通过发现这些会议的互动模式，可以揭示对会议的语义知识。现有方法使用树结构从会议中挖掘交互模式。然而，这种基于树的方法可能不会捕获交互之间的各种触发关系，并且可能不会将某个等级的参与者区分开在会议中不同等级的另一个参与者。因此，基于树的方法可能无法找到诸如关于相关交互的所有交互模式。在本文中，我们建议使用替代数据结构来挖掘与会议的相互作用模式 - 即定向的非循环图（DAG）。具体地，DAG捕获了会议中的交互之间的时间和触发关系。此外，要区分某个等级的一个参与者，我们将权重指向DAG中的节点。这样，可以将会议建模为加权DAG，可以从中发现加权频繁交互模式。实验结果表明，我们提出的基于DAG的互动模式与会议互动模式的有效性。

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