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.