Recognizing a Participant Behavior in a Multi-party Conversation: Detection of a Participant That Returns to a Discussion That Is Already Over
Abstract
In this paper, we discuss a task to recognize a participant’s behavior in a multi-party conversation. We focus on a detection task of a participant that returns to a discussion that is already over; we call the participant “BDH (Beat a Dead Horse) participant.” The target corpus contains 17 conversations about a topic with three participants, and one of three participants is the BDH participant. To detect the BDH participant, we apply machine learning methods. We compare three machine learning methods; naive bayes, decision tree, and support vector machines. In addition, we introduce a selection model based on the task setting. The experimental result shows the effectiveness of SVMs with our selection model.
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