Museli: A Multi-Source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue
Museli: A Multi-Source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue
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Museli：一种多源证据整合方法来进行自发对话的主题分割
DOI：
10.3115/1614049.1614052
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
C. Rosé
中科院分区：
文献类型：
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作者：
Jaime Arguello;C. Rosé
我们引入了一种新颖的主题分割方法，该方法将词汇衔接的主题转移证据与语言证据（例如片段初始贡献的句法独特特征）结合起来。我们的评估表明，即使应用于结构松散的自发对话，这种混合方法也优于最先进的算法。
We introduce a novel topic segmentation approach that combines evidence of topic shifts from lexical cohesion with linguistic evidence such as syntactically distinct features of segment initial contributions. Our evaluation demonstrates that this hybrid approach outperforms state-of-the-art algorithms even when applied to loosely structured, spontaneous dialogue.