Pronoun Resolution in Spoken Dialog
口语对话中的代词指代消解

This paper presents a two-stage pronoun resolution algorithm. It does not need to clean the testing corpus and predefine patterns manually. In the first stage of the algorithm, some new features and machine learning methods are used to classify pronouns into anaphoric and non-anaphoric ones. In the second stage, these two kinds of pronouns are resolved respectively. For the anaphoric ones, some methods are presented to extract distance, syntactic, and semantic features etc. For the non-anaphoric ones, the Right Frontier Rule is improved to do the resolution work. While testing the corpus published by Byron in 2004, this algorithm achieves a precision of 77.0% and a recall of 66.0%. Compared with the work of Byron, the algorithm is fully automatic, and the results are much better.