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 this http URL 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.

本论文提出基于实体的文档上下文过滤来构建输入并基于跨路径实体关系注意力的交叉文档关系提取模型，与现有方法比较，在 CodRED 数据集上取得至少 10％ 的 F1 值的提升，从而展示其在跨文档关系提取中的有效性。