A Hybrid Approach to Pronominal Anaphora Resolution in Arabic

One of the challenges in natural language processing is to determine which pronouns to be referred to their intended referents in the discourse. Performing anaphora resolution is considered as an important task for a number of natural language processing applications such as information extraction, question answering and text summarization. Most of the earlier works of anaphora resolution have been applied to English and other languages. However, the work done in Arabic is not sufficiently studied. In this study, a hybrid approach that combines different architectures for resolving pronominal anaphora in Arabic language is presented. The hybrid model adopted the strategy based on the combination of a rule-based and machine learning approach. The collection of anaphora and respective possible antecedents was identified in a rule-based manner with morphological information taken into account. In addition, the selection of the most probable candidate as the antecedent of the anaphor was done by machine learning based on a k-Nearest Neighbor (k-NN) approach. In this study, the appropriate features to be used in this task were determined and their effect on the performance of anaphora resolution was investigated. Experiments of the proposed method were performed using the corpus of the Quran annotated with pronominal anaphora. The experimental results indicate that the proposed hybrid approach is completely reasonable and feasible for Arabic pronominal anaphora resolution.