We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron's (2002) manually tuned system.
REF presented a machine-learning based pronoun resolution system for spoken dialogue (Switchboard corpus).
A Machine Learning Approach To Pronoun Resolution In Spoken Dialogue
Annual Meeting Of The Association For Computational Linguistics
Computer Science