A Public Reference Implementation of the RAP Anaphora Resolution Algorithm

TL;DR

This paper presents JavaRAP, a publicly available implementation of the RAP anaphora resolution algorithm, enabling benchmarking and comparison with other coreference resolution systems.

Contribution

It provides the first reference implementation of RAP that can be used for benchmarking and research in anaphora resolution.

Findings

JavaRAP achieves 57.9% accuracy on MUC-6 coreference task.

The implementation uses the Charniak parser for input.

JavaRAP can produce anaphora-antecedent pairs or annotated text.

Abstract

This paper describes a standalone, publicly-available implementation of the Resolution of Anaphora Procedure (RAP) given by Lappin and Leass (1994). The RAP algorithm resolves third person pronouns, lexical anaphors, and identifies pleonastic pronouns. Our implementation, JavaRAP, fills a current need in anaphora resolution research by providing a reference implementation that can be benchmarked against current algorithms. The implementation uses the standard, publicly available Charniak (2000) parser as input, and generates a list of anaphora-antecedent pairs as output. Alternately, an in-place annotation or substitution of the anaphors with their antecedents can be produced. Evaluation on the MUC-6 co-reference task shows that JavaRAP has an accuracy of 57.9%, similar to the performance given previously in the literature (e.g., Preiss 2002).