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

机译：使用有向无环图从会议中的人际互动挖掘频繁模式

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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，人与人之间的互动经常发生在讨论主题的会议中。通过发现这些会议的交互方式，可以揭示会议的语义知识。现有方法使用树结构从会议中挖掘交互模式。但是，这种基于树的方法可能无法捕获交互之间的所有触发关系，并且可能无法在会议中将某个级别的参与者与另一个级别的参与者区分开。因此，基于树的方法可能无法找到所有交互模式，例如与相关交互有关的模式。在本文中，我们建议使用替代数据结构（即有向无环图（DAG））从会议中挖掘交互模式。具体来说，DAG可以捕获会议中交互之间的时间关系和触发关系。此外，为了区分某个等级的参与者与其他参与者，我们将权重分配给DAG中的节点。这样，可以将会议建模为加权DAG，从中可以发现加权的频繁交互模式。实验结果表明，我们提出的基于DAG的方法从会议中挖掘交互模式的有效性。

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