Paper: Semi-supervised Speech Act Recognition in Emails and Forums

ACL ID D09-1130
Title Semi-supervised Speech Act Recognition in Emails and Forums
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

In this paper, we present a semi-supervised method for automatic speech act recogni- tion in email and forums. The major chal- lenge of this task is due to lack of labeled data in these two genres. Our method leverages labeled data in the Switchboard- DAMSL and the Meeting Recorder Dia- log Act database and applies simple do- main 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 sub- tree features, for semi-supervised learn- ing. Empirical results demonstrate that our model is effective in email and forum speech act recognition.