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[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
Recent advances in biomedical literature mining
The recent years have witnessed a rapid increase in the number of scientific articles in
biomedical domain. These literature are mostly available and readily accessible in …
biomedical domain. These literature are mostly available and readily accessible in …
Domain-specific language model pretraining for biomedical natural language processing
Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …
Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets
Inspired by the success of the General Language Understanding Evaluation benchmark, we
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …
Understanding customer satisfaction via deep learning and natural language processing
It is of utmost importance for marketing academics and service industry practitioners to
understand the factors that influence customer satisfaction. This study proposes a novel …
understand the factors that influence customer satisfaction. This study proposes a novel …
A survey of multi-label classification based on supervised and semi-supervised learning
M Han, H Wu, Z Chen, M Li, X Zhang - International Journal of Machine …, 2023 - Springer
Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …
Revisiting transformer-based models for long document classification
The recent literature in text classification is biased towards short text sequences (eg,
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …
Deep learning for religious and continent-based toxic content detection and classification
With time, numerous online communication platforms have emerged that allow people to
express themselves, increasing the dissemination of toxic languages, such as racism …
express themselves, increasing the dissemination of toxic languages, such as racism …
AI-based ICD coding and classification approaches using discharge summaries: A systematic literature review
The assignment of codes to free-text clinical narratives have long been recognised to be
beneficial for secondary uses such as funding, insurance claim processing and research …
beneficial for secondary uses such as funding, insurance claim processing and research …
[HTML][HTML] Optimal performance of Binary Relevance CNN in targeted multi-label text classification
In the context of multi-label text classification (MLTC), Binary Relevance (BR) stands out as
one of the most intuitive and frequently employed methodologies. It tackles the MLTC task by …
one of the most intuitive and frequently employed methodologies. It tackles the MLTC task by …