ECIFF: Event Causality Identification based on Feature Fusion

Event causality identification is an important task in natural language processing. However, this task is highly challenging due to the high dependency of event context, text semantic ambiguity and insignificant causality features between text events. These issues lead to the low precision of causal relationship identification between events. We propose an Event Causality Identification Based on Feature Fusion (ECIFF) to improve the causality identification precision between events by integrating the context, semantics, and syntax of natural language. Firstly, we utilize BERT to capture the contextual features of events in natural language, enhancing the contextual embedding of events in different contexts. Secondly, based on an adversarial generative graph representation method, ECIFF learns a massive amount of causal relationships in the CauseNet, which can enhance the semantic representation of causes and effects of events. Next, we exploit the shortest dependency path to shorten the length of sentences and inductively learn all possible syntactic dependency relationships. Finally, the contextual, semantic and syntactic features are fused to synthetically determine the causal relationships among events. The experimental results indicate that our proposed approach significantly outperforms the state-of-the-art method LSIN: on the CTBank dataset, the precision, recall and F1-score of our approach are improved by 1.6%, 3.2% and 2.4%; on the ESL dataset, the precision, recall and F1-score of our approach are improved by 4.0%, 4.7% and 4.3%.