Knowledge Graph Enhanced Event Extraction in Financial Documents

TL;DR

This paper introduces a novel event extraction framework for financial documents that leverages knowledge graphs and graph neural networks to improve extraction accuracy, especially in complex, multi-event scenarios.

Contribution

It is the first to embed a knowledge graph via GNNs into event extraction, enhancing performance over previous methods in financial document analysis.

Findings

Outperforms state-of-the-art by 5.3% F1-score on Chinese financial announcements

Effectively captures entity relations and attributes to improve event extraction

Demonstrates the benefit of integrating knowledge graphs at document level

Abstract

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.