Knowledge Graph Enhanced Event Extraction in Financial Documents
This addresses the problem of scattered and mixed event elements in financial documents for NLP practitioners, though it is incremental as it builds on prior methods by adding knowledge graph integration.
The paper tackles event extraction from financial documents by integrating a knowledge graph with a Graph Neural Network to capture relations between event elements, achieving a 5.3% improvement in F1-score over the state-of-the-art on Chinese financial announcements.
Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies. We showcase the enhancement to this task brought by utilizing the knowledge graph that captures entity relations and their attributes. We propose a first event extraction framework that embeds a knowledge graph through a Graph Neural Network and integrates the embedding with regular features, all at document-level. Specifically, for extracting events from Chinese financial announcements, our method outperforms the state-of-the-art method by 5.3% in F1-score.