Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction

Abstract

Event coreference resolution is a crucial component in Natural Language Processing (NLP) applications as it directly affects text summarization, machine translation, classification, and textual entailment. However, the research on this task for Arabic language is limited, compared to other languages such as English, Chinese and Spanish. This paper aims to review the state-of-the-art approaches in event coreference (EC) within the context of coreference resolution tasks, emphasizing the significance of EC in NLP. The focus is placed on the latest developments in Arabic language processing related to event coreference. To fill this gap, a comprehensive study of existing work is conducted, and new approaches are suggested. The paper highlights the challenges specific to Arabic event coreference resolution, such as the variability of verb forms, pronoun ambiguity, ellipsis and null arguments, lexical and morphological variation, lack of annotated resources, discourse and pragmatic context, and cultural and contextual sensitivity. Addressing these challenges requires a deep understanding of Arabic linguistics, advanced NLP techniques, and the availability of annotated resources. Furthermore, this paper examines the existing datasets and methods for Arabic event coreference and proposes an annotation scheme. By leveraging existing NLP algorithms and developing event coreference resolution systems tailored for Arabic, the accuracy and performance of NLP tasks can be significantly improved.