Multi-label text classification using attention-based graph neural network
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It
is observed that most MLTC tasks, there are dependencies or correlations among labels …
is observed that most MLTC tasks, there are dependencies or correlations among labels …
Label-specific document representation for multi-label text classification
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific …
In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific …
Multi-label text classification with latent word-wise label information
Z Chen, J Ren - Applied Intelligence, 2021 - Springer
Multi-label text classification (MLTC) is a significant task that aims to assign multiple labels to
each given text. There are usually correlations between the labels in the dataset. However …
each given text. There are usually correlations between the labels in the dataset. However …
Label-specific dual graph neural network for multi-label text classification
Multi-label text classification is one of the fundamental tasks in natural language processing.
Previous studies have difficulties to distinguish similar labels well because they learn the …
Previous studies have difficulties to distinguish similar labels well because they learn the …
Enhancing label correlation feedback in multi-label text classification via multi-task learning
In multi-label text classification (MLTC), each given document is associated with a set of
correlated labels. To capture label correlations, previous classifier-chain and sequence-to …
correlated labels. To capture label correlations, previous classifier-chain and sequence-to …
Multi-label text classification via joint learning from label embedding and label correlation
H Liu, G Chen, P Li, P Zhao, X Wu - Neurocomputing, 2021 - Elsevier
For the multi-label text classification problems with many classes, many existing multi-label
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …
Hierarchical graph transformer-based deep learning model for large-scale multi-label text classification
J Gong, Z Teng, Q Teng, H Zhang, L Du, S Chen… - IEEE …, 2020 - ieeexplore.ieee.org
Traditional methods of multi-label text classification, particularly deep learning, have
achieved remarkable results. However, most of these methods use word2vec technology to …
achieved remarkable results. However, most of these methods use word2vec technology to …
Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation
learning and are widely used in various text mining tasks such as large-scale multi-label text …
learning and are widely used in various text mining tasks such as large-scale multi-label text …
Label prompt for multi-label text classification
R Song, Z Liu, X Chen, H An, Z Zhang, X Wang… - Applied Intelligence, 2023 - Springer
Multi-label text classification has been widely concerned by scholars due to its contribution
to practical applications. One of the key challenges in multi-label text classification is how to …
to practical applications. One of the key challenges in multi-label text classification is how to …
A hybrid BERT model that incorporates label semantics via adjustive attention for multi-label text classification
L Cai, Y Song, T Liu, K Zhang - Ieee Access, 2020 - ieeexplore.ieee.org
The multi-label text classification task aims to tag a document with a series of labels.
Previous studies usually treated labels as symbols without semantics and ignored the …
Previous studies usually treated labels as symbols without semantics and ignored the …