Classifying Speech Acts using Verbal Response Modes

The driving vision for our work is to provide intelligent, automated assistance to users in understanding the status of their email conversations. Our approach is to create tools that enable the detec- tion and connection of speech acts across email messages. We thus require a mech- anism for tagging email utterances with some indication of their dialogic function. However, existing dialog act taxonomies as used in computational linguistics tend to be too task- or application-specific for the wide range of acts we find repre- sented in email conversation. The Ver- bal Response Modes (VRM) taxonomy of speech acts, widely applied for discourse analysis in linguistics and psychology, is distinguished from other speech act tax- onomies by its construction from cross- cutting principles of classification, which ensure universal applicability across any domain of discourse. The taxonomy cat- egorises on two dimensions, characterised as literal meaning and pragmatic mean- ing. In this paper, we describe a statisti- cal classifier that automatically identifies the literal meaning category of utterances using the VRM classification. We achieve an accuracy of 60.8% using linguistic fea- tures derived from VRM's human annota- tion guidelines. Accuracy is improved to 79.8% using additional features.