Document-level relation extraction via dual attention fusion and dynamic asymmetric loss
Abstract Document-level relation extraction (RE), which requires integrating and reasoning information to identify multiple possible relations among entities. However, previous research typically performed reasoning on heterogeneous graphs and set a global threshold for multiple relations classifica...
Saved in:
Main Authors: | Xiaoyao Ding, Dongyan Ding, Gang Zhou, Jicang Lu, Taojie Zhu |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-11-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01632-8 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos
by: S Deepika, et al.
Published: (2025-01-01) -
Document-Level Neural TTS Using Curriculum Learning and Attention Masking
by: Sung-Woong Hwang, et al.
Published: (2021-01-01) -
Attention-based interactive multi-level feature fusion for named entity recognition
by: Yiwu Xu, et al.
Published: (2025-01-01) -
Exploring horses’ (Equus caballus) gaze and asymmetric ear position in relation to human attentional cues
by: Gabriela Barrera, et al.
Published: (2024-10-01) -
A Fusion Method Incorporating Dual-Attention Mechanism and Transfer Learning Into UNet++ for Remote Sensing Image Coastline Extraction
by: Yanru Song, et al.
Published: (2025-01-01)