论文标题
CoreFDRE：文档级别的关系提取与核心分辨率
CorefDRE: Document-level Relation Extraction with coreference resolution
论文作者
论文摘要
文档级的关系提取是从由多个句子组成的文档中提取关系事实，在该文档中，代词交叉句子是一种无处不在的现象，即单一句子。但是，以前的大多数作品都集中在提及核心分辨率上，除了代词外，很少关注提及普罗诺恩的核心和捕获关系。为了代表代词的多句子特征，我们在动态构造异质图以增强语义信息时利用核心信息来模仿人类的阅读过程。由于该代词在图中众所周知，因此引入了提及的核心分辨率来计算代词和相应提及之间的亲和力，并提出了噪声抑制机制来减少代词引起的噪声。在公共数据集，DOCRED，DIALOGRE和MPDD上进行的实验表明，基于Graph推理网络的CoreF感知DOC级关系提取提取胜过最新的。
Document-level relation extraction is to extract relation facts from a document consisting of multiple sentences, in which pronoun crossed sentences are a ubiquitous phenomenon against a single sentence. However, most of the previous works focus more on mentions coreference resolution except for pronouns, and rarely pay attention to mention-pronoun coreference and capturing the relations. To represent multi-sentence features by pronouns, we imitate the reading process of humans by leveraging coreference information when dynamically constructing a heterogeneous graph to enhance semantic information. Since the pronoun is notoriously ambiguous in the graph, a mention-pronoun coreference resolution is introduced to calculate the affinity between pronouns and corresponding mentions, and the noise suppression mechanism is proposed to reduce the noise caused by pronouns. Experiments on the public dataset, DocRED, DialogRE and MPDD, show that Coref-aware Doc-level Relation Extraction based on Graph Inference Network outperforms the state-of-the-art.
