Decision Detection using Hierarchical Graphical Models

In Proceedings of the 48th Annual Meeting of Association for Computational Linguistics (ACL 2010) , Uppsala, Sweden.

Publication date: January 31, 2010

Trung Bui, S. Peters

We investigate hierarchical graphical models (HGMs) for automatically detecting decisions in multi-party discussions. Several types of dialogue act (DA) are distinguished on the basis of their roles in formulating decisions. HGMs enable us to model dependencies between observed features of discussions, decision DAs, and sub-dialogues that result in a decision. For the task of detecting decision regions, an HGM classifier was found to outperform non-hierarchical graphical models and support vector machines, raising the F1-score to 0.80 from 0.55.

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