@inproceedings{09d2b61a2f9e4ff4b867794f1455fc62,
title = "SaGCN: Structure-Aware Graph Convolution Network for Document-Level Relation Extraction",
abstract = "Document-level Relation Extraction(DocRE) aims at extracting semantic relations among entities in documents. However, current models lack long-range dependency information and the reasoning ability to extract essential structure information from the text. In this paper, we propose SaGCN, a Structure-aware Graph Convolution Network, extracting relation with explicit and implicit dependency structure. Specifically, we generate the implicit graph by sampling from a discrete and continuous distribution, then dynamically fuse the implicit soft structure with the dependent hard structure. Experimental results of SaGCN outperform the performance achieved by current state-of-the-art various baseline models on the DocRED dataset.",
keywords = "Document-level relation extraction, Graph neural network, Structure-aware information injection",
author = "Shuangji Yang and Taolin Zhang and Danning Su and Nan Hu and Wei Nong and Xiaofeng He",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 ; Conference date: 11-05-2021 Through 14-05-2021",
year = "2021",
doi = "10.1007/978-3-030-75768-7\_30",
language = "英语",
isbn = "9783030757670",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "377--389",
editor = "Kamal Karlapalem and Hong Cheng and Naren Ramakrishnan and Agrawal, \{R. K.\} and Reddy, \{P. Krishna\} and Jaideep Srivastava and Tanmoy Chakraborty",
booktitle = "Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings",
address = "德国",
}