Research on Distance Information for Anaphora Resolution
Qiaoming Zhu · Zhongwen xinxi xuebao · 2008
Anaphora resolution plays an important role in natural language processing,which involves recognition of named entities,nominal phrase and pronoun anaphora etc.This paper presents a machine learning approach to anaphora resolution with special focus on the distance information between the anaphor and the antecedent candidate.Traditionally,the distance between anaphor and candidate is only adopted as a feature in machine learning approaches,without taking into account its contribution in the antecedent candidate generation.In this paper,the distance information is explored in details by either incorporating it as a feature in the learning algorithm(such as the maximum entropy model and the SVM model) or applying it as a hard constraint in the antecedent candidate generation.Evaluation on the MUC-6 benchmark corpus shows that proper handling of the distance information can much improve the performance and our system achieves the F1-measure of 68.7,which outperforms other similar systems.