2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-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
SessionHLT-13: Information Extraction
LocationGather.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
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.