Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413673 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Relation extraction, Named entity recognition, Graph convolutional networks, Text mining, Joint modeling framework | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haozhuang Liu | 1 | 0 | 1.01 |
| Ziran Li | 2 | 1 | 3.06 |
| Dongming Sheng | 3 | 0 | 1.35 |
| Haitao Zheng | 4 | 6 | 3.10 |
| Shen Ying | 5 | 73 | 23.48 |