Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings
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DOI：
10.1109/tkde.2010.224
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发表时间：
2012-04
影响因子：
8.9
通讯作者：
Zhiwen Yu;Zhiyong Yu;Xingshe Zhou;Christian Becker;Yuichi Nakamura
中科院分区：
文献类型：
--
作者：
Zhiwen Yu;Zhiyong Yu;Xingshe Zhou;Christian Becker;Yuichi Nakamura
发现语义知识对于理解和解释人们如何在会议讨论中互动具有重要意义。在本文中，我们提出了一种基于捕获的面对面会议内容来提取人类交互频繁模式的挖掘方法。人与人的互动，如提出想法、发表评论和表达积极的意见，表明用户对讨论中的某个主题或角色的意图。讨论会话中的人工交互流被表示为树。设计了基于树的交互挖掘算法，用于分析树的结构和提取交互流模式。实验结果表明，我们可以成功地提取出一些有趣的模式，这些模式对于解释会议讨论中的人类行为是有用的，例如确定频繁的交互、典型的交互流程以及不同类型交互之间的关系。
Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Tree-based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.