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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Unsupervised and self-supervised deep learning approaches for biomedical text mining.

Mohamed Nadif¹, François Role¹

¹Université de Paris, CNRS, Centre Borelli, France.

Briefings in Bioinformatics

|February 11, 2021

Summary

This summary is machine-generated.

Deep learning, including unsupervised and self-supervised learning, enhances biomedical text mining. These methods improve information extraction and document clustering, aiding researchers in navigating vast scientific literature.

Keywords:

deep learning self-supervised learning text mining unsupervised learning

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Area of Science:

Biomedical informatics
Computational linguistics
Machine learning

Background:

The rapid growth of biomedical literature necessitates advanced tools for information extraction.
Traditional supervised learning methods are well-established, but unsupervised and self-supervised learning offer novel approaches.

Purpose of the Study:

To survey the advancements in unsupervised and self-supervised learning for biomedical text mining.
To highlight the benefits of these learning paradigms in handling large-scale scientific data.

Main Methods:

Utilizing deep neural networks for data representation.
Applying unsupervised learning techniques, such as clustering, for document organization.
Leveraging self-supervised learning to generate effective word embeddings using transformer architectures.

Main Results:

Deep learning creates clustering-friendly data representations.
Self-supervised learning yields powerful word embeddings.
These embeddings enhance the performance of downstream supervised tasks like classification.

Conclusions:

Unsupervised and self-supervised learning are crucial for efficient biomedical text mining.
These methods facilitate the exploration and management of extensive scientific literature.
The integration of advanced learning techniques significantly boosts information extraction capabilities.