| Paper ID | HLT-13.1 |
| Paper Title |
Multi-Entity Collaborative Relation Extraction |
| Authors |
Haozhuang Liu, Ziran Li, Dongming Sheng, Hai-Tao Zheng, Tsinghua University, China; Ying Shen, Sun Yat-Sen University, China |
| 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 |
Multi-entity collaborative relationship extraction is an important but challenging task, which has been attracting a lot of interest and poses significant issues in front of systems aimed at natural language understanding. Instead of designing specific models for single relationship extraction tasks, this paper aims to propose a general framework to extract multiple relations among multiple entities in unstructured text by taking advantage of existing models. Based on performing named entity recognition and relation extraction collaboratively, the framework exploits correlations and information propagation among words and relations in a graph network to grasp fundamental features for final classification. The experimental results on two real-world datasets demonstrate that our framework has remarkable applicability and generalizability, and consistently outperforms the strong competitors by a noticeable margin for multi-entity relation extraction. |
