A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects (2411.10371v3)

Abstract: Event Causality Identification (ECI) has emerged as a pivotal task in NLP, aimed at automatically detecting causal relationships between events in text. In this comprehensive survey, we systematically elucidate the foundational principles and technical frameworks of ECI, proposing a novel classification framework to categorize and clarify existing methods. {We discuss associated challenges, provide quantitative evaluations, and outline future directions for this dynamic and rapidly evolving field. We first delineate key definitions, problem formalization, and evaluation protocols of ECI. Our classification framework organizes ECI methods based on two primary tasks: Sentence-level Event Causality Identification (SECI) and Document-level Event Causality Identification (DECI). For SECI, we review methods including feature pattern-based matching, machine learning-based classification, deep semantic encoding, prompt-based fine-tuning, and causal knowledge pre-training, alongside common data augmentation strategies. For DECI, we focus on techniques such as deep semantic encoding, event graph reasoning, and prompt-based fine-tuning. We dedicate specific discussions to advancements in multi-lingual and cross-lingual ECI as well as zero-shot ECI leveraging LLMs. Furthermore, we analyze the strengths, limitations, and unresolved challenges of each method. Extensive quantitative evaluations are conducted on four benchmark datasets to assess various ECI methods. Finally, we explore future research directions.