tailieunhanh - Báo cáo khoa học: "A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue"

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. | A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue Michael Strube and Christoph Miiller European Media Laboratory GmbH Villa Bosch SchloB-Wolfsbrunnenweg 33 69118 Heidelberg Germany @ Abstract We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP-and 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. 1 Introduction Corpus-based methods and machine learning techniques have been applied to anaphora resolution in written text with considerable success Soon et al. 2001 Ng Cardie 2002 among others . It has been demonstrated that systems based on these approaches achieve a performance that is comparable to hand-crafted systems. Since they can easily be applied to new domains it seems also feasible to port a given corpus-based anaphora resolution system from written text to spoken dialogue. This paper describes the extensions and adaptations needed for applying our anaphora resolution system Muller et al. 2002 Strube et al. 2002 to pronoun resolution in spoken dialogue. There are important differences between written text and spoken dialogue which have to be accounted for. The most obvious difference is that in spoken dialogue there is an abundance of personal and demonstrative pronouns with non-NP-antecedents or no antecedents at all. Corpus studies have shown that a significant amount of pronouns in spoken dialogue have non-NP-antecedents Byron Allen 1998 report that about 50 of the pronouns in the TRAINS93 corpus have non-NP-antecedents. Eckert Strube 2000 note that only about 45 of the pronouns in a set of Switchboard dialogues have NP-antecedents. The remainder consists of 22 which have non-NP-antecedents and 33 without