A Statistical Approach to Anaphora Resolution

Niyu Ge, John Hale, and Eugene Charniak
This paper presents an algorithm for identifying pronominal anaphora and two experiments based upon this algorithm. We incorporate multiple anaphora resolution factors into a statistical framework --- specifically the distance between the pronoun and the proposed antecedent, gender/number/animaticity of the proposed antecedent, governing head information and noun phrase repetition. We combine them into a single probability that enables us to identify the referent. Our first experiment shows the relative contribution of each source of information and demonstrates a success rate of 82.9\% for all sources combined. The second experiment investigates a method for unsupervised learning of gender/number/animaticity information. We present some experiments illustrating the accuracy of the method and note that with this information added, our pronoun resolution method achieves 84.2\% accuracy.