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%.

Causal relationships between events can reflect the historical evolution of events and provide an important reference for predicting future trends. Script prediction methods based on event graphs often struggle to adequately consider the complex interdependencies among events, leading to prediction biases. The Script Event Prediction Based on Causal Generalization Learning (SEPCG) method has been proposed to enhance the accuracy of script event prediction. SEPCG uses the graph attention network to learn the direct causal relationship similarity between known events and candidate events, the direct result event similarity between known events and candidate events, and utilizes double similarity generalization to learn the predicate type between known events and candidate events. SEPCG uses a Neural Tensor Network to learn parameter-level event embeddings and improve the model’s sensitivity to parameter-level changes. Finally, based on the generalized event embeddings, the BiLSTM network is used to simultaneously learn the forward contextual information from the known event to the candidate event direction, i.e., the cause to the result information, and the reverse contextual information from the candidate event to the known event direction, i.e., the result to the cause information. The BiLSTM is used to capture the temporal information of event chains at different levels. The effectiveness of the model is verified on the NYT dataset, with a 1.84% improvement in accuracy compared to the best baseline.