| Paper ID | HLT-13.5 |
| Paper Title |
MORE: A METRIC LEARNING BASED FRAMEWORK FOR OPEN-DOMAIN RELATION EXTRACTION |
| Authors |
Yutong Wang, Tsinghua Shenzhen International Graduate School, Tsinghua University, China; Renze Lou, Department of Computer Science, Zhejiang University City College, China; Kai Zhang, Department of Computer Science and Technology, Tsinghua University, China; Mao Yan Chen, Yujiu Yang, Tsinghua Shenzhen International Graduate School, Tsinghua University, Canada |
| Session | HLT-13: Information Extraction |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-MLMD] Machine Learning Methods for Language |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Open relation extraction (OpenRE) is a task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). The framework utilizes deep metric learning (DML) to obtain rich supervision signals from labeled data and drive the neural model to learn semantic relational representation directly. Experiments result in two real-world datasets show that our method outperforms other state-of-the-art baselines. |
