@inproceedings{543d6d1aeefe4d0cac19e368271879be,
title = "Using Dilated Residual Network to Model Distantly Supervised Relation Extraction",
abstract = "Distantly supervised relation extraction has been widely used to find relational facts in the text. However, distant supervision inevitably brings in noise that can lead to a bad relation contextual representation. In this paper, we propose a deep dilated residual network (DRN) model to address the noise of in distantly supervised relation extraction. Specifically, we design a module which employs dilated convolution in cascade to capture multi-scale context features by adopting multiple dilation rates. By combining them with residual learning, the model is more powerful than traditional CNN model. Our model significantly improves the performance for distantly supervised relation extraction on the large NYT-Freebase dataset compared to various baselines.",
keywords = "Distant supervision, Knowledge graph, Relation extraction",
author = "Lei Zhan and Yan Yang and Pinpin Zhu and Liang He and Zhou Yu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA ; Conference date: 22-04-2019 Through 25-04-2019",
year = "2019",
doi = "10.1007/978-3-030-18590-9\_75",
language = "英语",
isbn = "9783030185893",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "500--504",
editor = "Guoliang Li and Jun Yang and Joao Gama and Juggapong Natwichai and Yongxin Tong",
booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",
address = "德国",
}