Semi-supervised Speech Act Recognition in Emails and Forums

In this paper, we present a semi-supervised method for automatic speech act recognition in email and forums. The major challenge of this task is due to lack of labeled data in these two genres. Our method leverages labeled data in the SwitchboardDAMSL and the Meeting Recorder Dialog Act database and applies simple domain adaptation techniques over a large amount of unlabeled email and forum data to address this problem. Our method uses automatically extracted features such as phrases and dependency trees, called subtree features, for semi-supervised learning. Empirical results demonstrate that our model is effective in email and forum speech act recognition.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Minwoo Jeong, Chin-Yew Lin, Gary Geunbae Lee
• Keywords: EMNLP 2009, Natural Language Processing, Recorder Dialog Act, Semi-supervised Method, Speech Act Recognition