Label-text bi-attention capsule networks model for multi-label text classification-Reference-Cited by-同舟云学术

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Label-text bi-attention capsule networks model for multi-label text classification

Published:2024-07 Issue: Volume:588 Page:127671
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Wang Gang,Du Yajun,Jiang Yurui,Liu Jia,Li Xianyong,Chen Xiaoliang,Gao Hongmei,Xie Chunzhi,Lee Yan-li

Funder

Sichuan Province Science and Technology Support Program

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference49 articles.

1. Combining Naive Bayes and n-Gram language models for text classification;Peng,2003

2. Binary relevance efficacy for multilabel classification;Luaces;Prog. Artif. Intell.,2012

3. Constructing better prototype generators with 3D CNNs for few-shot text classification;Wang;Expert Syst. Appl.,2023

4. C-BDCLSTM: A false emotion recognition model in micro blogs combined Char-CNN with bidirectional dilated convolutional LSTM;Hou;Appl. Soft Comput.,2022

5. UD_BBC: Named entity recognition in social network combined BERT-BiLSTM-CRF with active learning;Li;Eng. Appl. Artif. Intell.,2022

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1. Extensive experimental comparison among multilabel methods focused on ranking performance;Information Sciences;2024-09

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