Writing Style Aware Document-level Event Extraction

TL;DR

This paper introduces a novel document-level event extraction method that leverages writing style patterns, modeled as Role-Rank Distributions, to improve accuracy over existing token-level classification approaches.

Contribution

It proposes a new way to incorporate writing style as Role-Rank Distributions into event extraction, enhancing performance on real-world datasets.

Findings

Outperforms state-of-the-art methods on multiple datasets

Captures writing style patterns effectively

Improves role classification accuracy

Abstract

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring the writing styles of documents. The writing style is a special way of content organizing for documents and it is relative fixed in documents with a special field (e.g. financial, medical documents, etc.). We argue that the writing style contains important clues for judging the roles for tokens and the ignorance of such patterns might lead to the performance degradation for the existing works. To this end, we model the writing style in documents as a distribution of argument roles, i.e., Role-Rank Distribution, and propose an event extraction model with the Role-Rank Distribution based Supervision Mechanism to capture this pattern through the supervised training process of an event extraction task. We compare our model with state-of-the-art methods on several real-world datasets. The empirical results show that our approach outperforms other alternatives with the captured patterns. This verifies the writing style contains valuable information that could improve the performance of the event extraction task.