Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
This work addresses event factuality prediction for natural language processing, but it appears incremental as it builds on prior methods by improving integration.
The paper tackled event factuality prediction by integrating syntactic and semantic information more effectively using a novel graph-based neural network, demonstrating advantages in experiments.
Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.