Posting Act Tagging Using Transformation-Based Learning

Abstract

In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat conversations. We define a list of posting tags that have proven useful in chat-conversation analysis. We describe the templates used for posting act tagging in the context of template selection. We extend traditional approaches used in part-of-speech tagging and dialogue act tagging by incorporating regular expressions into our templates. We close with a presentation of results that compare favorably with the application of TBL in dialogue act tagging.