Joint Extraction of Events and Entities within a Document Context

TL;DR

This paper introduces a joint extraction method for events and entities across entire documents, leveraging document-level context to improve accuracy over existing sentence-level approaches.

Contribution

It proposes a novel joint inference model that captures dependencies among events, entities, and their relations across documents, enhancing extraction performance.

Findings

Outperforms state-of-the-art event extraction methods

Significantly improves entity extraction accuracy

Utilizes document-level context for better predictions

Abstract

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.