Learning Label Independence and Relevance for Multi-Label Biomedical Text Classification-Reference-Cited by-同舟云学术

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Learning Label Independence and Relevance for Multi-Label Biomedical Text Classification

Published:2022-10-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Chen Zihao¹,Peng Jing¹

Affiliation:

1. Wuhan University Of Technology,School of Computer Science and Artificial Intelligence,Wuhan,Hubei,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9945068/9945069/09945404.pdf?arnumber=9945404

Reference30 articles.

1. LitCovid: an open database of COVID-19 literature

2. Augmenting word embeddings through external knowledge-base for biomedical application

3. Generating Medical Hypotheses Based on Evolutionary Medical Concepts

4. MeSH Up: effective MeSH text classification for improved document retrieval

5. MEDLINE MeSH indexing

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MCICT: Graph convolutional network-based end-to-end model for multi-label classification of imbalanced clinical text;Biomedical Signal Processing and Control;2024-05

2. Transfer Learning Approach to Multilabel Biomedical Literature Classification using Transformer Models;2023 IEEE 8th International Conference for Convergence in Technology (I2CT);2023-04-07

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