2020/09/29 by Shuang Zeng, Zeng, Shuang, Runxin Xu +5 · 12 citations
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Artificial intelligence #Code (set theory) #Computation and Language (cs.CL) #Computer science #Data mining #FOS: Computer and information sciences #Graph #Inference #Information extraction #Information retrieval #Knowledge graph #Machine Learning (cs.LG) #Natural Language Processing Techniques #Natural language processing #Programming language #Relation (database) #Relationship extraction #Sentence #Theoretical computer science #Topic Modeling #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2009.13752
published in arXiv (Cornell University) (Cornell University) · Accepted as long paper to appear at the EMNLP 2020 main conference, 11 pages, 3 figures
arxiv created 2020/09/29 · openalex publication_date 2020/09/29 · arxiv updated 2020/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entity-level graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/DreamInvoker/GAIN .