Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification
We propose a novel sequence-to-sequence model for multi-label text classification, based on a “parallel encoding, serial decoding” strategy. The model combines a convolutional neural network and self-attention in parallel as the encoder to extract fine-grained local neighborhoo...
Main Authors: | Zhenyu Yang, Guojing Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8879472/ |
Similar Items
-
UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification
by: Bo Ning, et al.
Published: (2023-04-01) -
A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification
by: Linkun Cai, et al.
Published: (2020-01-01) -
Multi-granularity sequence generation for hierarchical image classification
by: Xinda Liu, et al.
Published: (2024-01-01) -
Enhancing the Performance of Multi-Category Text Classification via Label Relation Mining
by: Yun Wang, et al.
Published: (2024-01-01) -
HMATC: Hierarchical multi-label Arabic text classification model using machine learning
by: Nawal Aljedani, et al.
Published: (2021-09-01)