Relation extraction (RE) has recently moved from the sentence-level to document-level, which requires aggregating document information and using entities and mentions for reasoning. Existing works put entity nodes and mention nodes with similar representations in a document-level graph, whose complex edges may incur redundant information. Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). Specifically, a simplified document-level graph is constructed to model the semantic information of all mentions and sentences in a document, and an entity-level graph is designed to explore relations of long-distance cross-sentence entity pairs. Experimental results show that GRACR achieves excellent performance on two public datasets of document-level RE. It is especially effective in extracting potential relations of cross-sentence entity pairs. Our code is available at https://github.com/UESTC-LHF/GRACR.
翻译:最近,关系提取(RE)已经从句级转向文件级,这要求汇总文件信息,使用实体并提及理由; 现有工作在文件级图中列出实体节点并提及具有类似表述的节点,其复杂边缘可能带来多余的信息; 此外,现有研究只侧重于实体级推理路径,而没有考虑各实体之间的全球互动; 为达到这些目的,我们提出一个新的文件级RE模式,与Graph信息汇总和交叉陈述理由网络(GRACR)合作; 具体地说,设计了一个简化的文件级图表,以模拟文件中所有提及和句子的语义信息,而实体级图旨在探索长距离跨句子实体对对子的关系; 实验结果表明,GRACR在文件级对子的两个公共数据集上取得了出色的表现; 在提取跨句子实体对子的潜在关系方面特别有效。 我们的代码可在https://github.com/UESTC-LHF/GRACRCR。</s>