Recovering Implicit Thread Structure in Newsgroup Style Conversations

Yi-Chia Wang, Mahesh Joshi, William W. Cohen, C. Rosé
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引用次数: 67

Abstract

On-line discussions are composed of multiple inter-woven threads, regardless of whether that threaded structure is made explicit in the representation and presentation of the conversational data. Recovering the thread structure is valuable since it makes it possible to isolate discussion related to specific subtopics or related to particular conversational goals. In prior work, thread structure has been reconstructed using explicit meta-data features such as posted by and reply to relationships. The contribution of this paper is a novel approach to recovering thread structure in discussion forums where this explicit meta-data is missing. This approach uses a graph-based representation of a collection of messages where connections between messages are postulated based on inter-message similarity. We evaluate three variations of this simple baseline approach that exploit in different ways the temporal relationships between messages. The results show that the three proposed approaches outperform the simple threshold-cut baseline.
新闻组对话中隐式线程结构的恢复
在线讨论由多个相互交织的线程组成,而不管该线程结构在会话数据的表示和表示中是否明确。恢复线程结构是有价值的,因为它可以隔离与特定子主题或与特定会话目标相关的讨论。在之前的工作中,线程结构已经使用明确的元数据特征(如张贴关系和回复关系)进行了重构。本文的贡献在于提供了一种新颖的方法,可以在缺少显式元数据的论坛中恢复线程结构。此方法使用基于图的消息集合表示,其中消息之间的连接是基于消息间相似性假设的。我们评估了这种简单基线方法的三种变体,它们以不同的方式利用消息之间的时间关系。结果表明,这三种方法都优于简单的阈值分割基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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