[HTML][HTML] PHQ-aware depressive symptoms identification with similarity contrastive learning on social media
Depressive symptoms identification on social media aims to identify posts from social media
expressing symptoms of depression. This can be beneficial for developing mental health …
expressing symptoms of depression. This can be beneficial for developing mental health …
Multi-label text classification based on semantic-sensitive graph convolutional network
D Zeng, E Zha, J Kuang, Y Shen - Knowledge-Based Systems, 2024 - Elsevier
Abstract Multi-Label Text Classification (MLTC) is an important but challenging task in the
field of natural language processing. In this paper, we propose a novel method, Semantic …
field of natural language processing. In this paper, we propose a novel method, Semantic …
Label-text bi-attention capsule networks model for multi-label text classification
Multi-label text classification (MLTC) is the process of establishing relationships between
documents and their corresponding labels. Previous research has focused on mining textual …
documents and their corresponding labels. Previous research has focused on mining textual …
Accurate use of label dependency in multi-label text classification through the lens of causality
Abstract Multi-Label Text Classifiction (MLTC) aims to assign the most relevant labels to
each given text. Existing methods demonstrate that label dependency can help to improve …
each given text. Existing methods demonstrate that label dependency can help to improve …
All is attention for multi-label text classification
Z Liu, Y Huang, X Xia, Y Zhang - Knowledge and Information Systems, 2024 - Springer
Multi-label text classification (MLTC) is a key task in natural language processing. Its
challenge is to extract latent semantic features from text and effectively exploit label …
challenge is to extract latent semantic features from text and effectively exploit label …
Dual-view graph convolutional network for multi-label text classification
X Li, B You, Q Peng, S Feng - Applied Intelligence, 2024 - Springer
Multi-label text classification refers to assigning multiple relevant category labels to each
text, which has been widely applied in the real world. To enhance the performance of multi …
text, which has been widely applied in the real world. To enhance the performance of multi …
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
A Audibert, A Gauffre, MR Amini - Joint European Conference on Machine …, 2024 - Springer
Learning an effective representation in multi-label text classification (MLTC) is a significant
challenge in natural language processing. This challenge arises from the inherent …
challenge in natural language processing. This challenge arises from the inherent …
TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification
F Zhao, Q Ai, X Li, W Wang, Q Gao, Y Liu - Neural Processing Letters, 2024 - Springer
Extreme multi-label text classification (XMTC) annotates related labels for unknown text from
large-scale label sets. Transformer-based methods have become the dominant approach for …
large-scale label sets. Transformer-based methods have become the dominant approach for …
LSPCL: Label-specific supervised prototype contrastive learning for multi-label text classification
G Wang, Y Du, Y Jiang - Knowledge-Based Systems, 2024 - Elsevier
Contrastive learning has led to further breakthroughs in feature representation. However,
text datasets often suffer from class imbalance issues, resulting in suboptimal feature …
text datasets often suffer from class imbalance issues, resulting in suboptimal feature …
Domain-invariant feature learning with label information integration for cross-domain classification
Traditional methods for unsupervised cross-domain classification learn a common low-
dimensional subspace using images from a well-labeled source domain and an unlabeled …
dimensional subspace using images from a well-labeled source domain and an unlabeled …