.png){kind=icon}
.png){kind=icon}
Return
A Cross-Attention Fusion Based Graph Convolution Auto-Encoder for Open Relation Extraction
.png){kind=icon}
PRE
AI
DOI：10.1109/TASLP.2022.3226680.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Open Relation Extraction (OpenRE) aims at clustering relation instances to extract relation types. By learning relation patterns between named entities, it clusters semantically equivalent patterns into a unified relation cluster. Existing clustering-based OpenRE methods only consider the information of the instance itself, ignoring knowledge of any relations between instances. Therefore, a Cross-Attention Fusion based Graph Convolution Auto-Encoder (CAGCE) method for Open Relation Extraction is proposed. The Auto-Encoder learns the semantic information of the sentence instance itself, and the Graph Convolution Network learns the relational similarity information between sentences. Then, the two heterogeneous representations are crossed and fused layer-by-layer through a cross-attention fusion mechanism. Finally, the fused features are used for clustering to form the relation types. A comparison with baseline models using the FewRel and NYT-FB datasets shows the effectiveness and superiority of the proposed method.
Keywords:
Attention mechanism
auto-encoder
graph convolution network
open relation extraction
Journal
I
IF:
5.1
Papers:
2.6K
Citations:
1.1W