Entity-centered Cross-document Relation Extraction

TL;DR

This paper introduces an entity-centered approach for cross-document relation extraction that filters relevant context and models inter-path entity relations, significantly improving accuracy over previous methods.

Contribution

It proposes a novel entity-based document filtering technique and a cross-path entity relation attention mechanism for enhanced cross-document RE.

Findings

Outperforms state-of-the-art methods by at least 10% in F1 score on CodRED dataset.

Effectively filters relevant information using bridge entities.

Models interactions between text paths to improve relation extraction.

Abstract

Relation Extraction (RE) is a fundamental task of information extraction, which has attracted a large amount of research attention. Previous studies focus on extracting the relations within a sentence or document, while currently researchers begin to explore cross-document RE. However, current cross-document RE methods directly utilize text snippets surrounding target entities in multiple given documents, which brings considerable noisy and non-relevant sentences. Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths.In this paper, we aim to address both of these shortages and push the state-of-the-art for cross-document RE. First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths. Second, we propose a cross-document RE model based on cross-path entity relation attention, which allow the entity relations across text paths to interact with each other. We compare our cross-document RE method with the state-of-the-art methods in the dataset CodRED. Our method outperforms them by at least 10% in F1, thus demonstrating its effectiveness.