Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature-Reference-Cited by-同舟云学术

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Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature

Published:2023-10 Issue: Volume:146 Page:104499
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Nentidis Anastasios^ORCID,Chatzopoulos Thomas^ORCID,Krithara Anastasia^ORCID,Tsoumakas Grigorios^ORCID,Paliouras Georgios^ORCID

Funder

Hellenic Foundation for Research and Innovation

U.S. National Library of Medicine

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference27 articles.

1. Overview of BioASQ 2022: The tenth BioASQ challenge on large-scale biomedical semantic indexing and question answering;Nentidis,2022

2. Domain-specific language model pretraining for biomedical natural language processing;Gu;ACM Trans. Comput. Healthc.,2022

3. The NLM indexing initiative’s medical text indexer;Aronson;Stud. Health Technol. Inf.,2004

4. Beyond MeSH: Fine-grained semantic indexing of biomedical literature based on weak supervision;Nentidis;Inf. Process. Manage.,2020

5. Coarse2Fine: Fine-grained text classification on coarsely-grained annotated data;Mekala,2021

Cited by 1 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. Semantics-enabled biomedical literature analytics;Journal of Biomedical Informatics;2024-02

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