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A Probabilistic Model of Meetings That Combines Words and Discourse Features

机译:结合词和话语特征的会议概率模型

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In order to determine the points at which meeting discourse changes from one topic to another, probabilistic models were used to approximate the process through which meeting transcripts were produced. Gibbs sampling was used to estimate the values of random variables in the models, including the locations of topic boundaries. This paper shows how discourse features were integrated into the Bayesian model and reports empirical evaluations of the benefit obtained through the inclusion of each feature and of the suitability of alternative models of the placement of topic boundaries. It demonstrates how multiple cues to segmentation can be combined in a principled way, and empirical tests show a clear improvement over previous work.
机译:为了确定会议讨论话题从一个话题变为另一个话题的要点,使用了概率模型来估算会议记录的生成过程。 Gibbs采样用于估计模型中随机变量的值,包括主题边界的位置。本文展示了话语特征如何整合到贝叶斯模型中,并报告了对通过包含每个特征所获得的收益以及主题边界位置的替代模型的适用性进行的经验评估。它演示了如何以原则性方式组合多个分割线索,并且经验测试显示,与以前的工作相比有明显的改进。

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