论文标题
使用笑声发生的主题结构的混合框架
A Hybrid Framework for Topic Structure using Laughter Occurrences
论文作者
论文摘要
会话话语的连贯性取决于语言和副语言现象。在这项工作中，我们通过多层层次结构将副语言和语言知识结合到混合框架中。因此，它输出了话语级的主题结构。笑声的发生用作ICSI数据库的多方成绩单中的副语言信息。提出了一种基于聚类的算法，该算法从两个独立的，优化的簇中选择了最佳的主题段群集，即分层集聚聚类和$ K $ -MEDOIDS。然后，它与现有的基于词汇凝聚力的贝叶斯主题分割框架进行了迭代杂交。混合方法改善了两种独立方法的性能。这导致对主题结构与话语关系结构之间的相互作用进行了简要研究。这种无培训的主题结构方法可用于在线了解口语对话。
Conversational discourse coherence depends on both linguistic and paralinguistic phenomena. In this work we combine both paralinguistic and linguistic knowledge into a hybrid framework through a multi-level hierarchy. Thus it outputs the discourse-level topic structures. The laughter occurrences are used as paralinguistic information from the multiparty meeting transcripts of ICSI database. A clustering-based algorithm is proposed that chose the best topic-segment cluster from two independent, optimized clusters, namely, hierarchical agglomerative clustering and $K$-medoids. Then it is iteratively hybridized with an existing lexical cohesion based Bayesian topic segmentation framework. The hybrid approach improves the performance of both of the stand-alone approaches. This leads to the brief study of interactions between topic structures with discourse relational structure. This training-free topic structuring approach can be applicable to online understanding of spoken dialogs.
