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Found 6 papers, 5 papers with code
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models
Recently, using large pretrained Transformer models for transfer learning tasks has evolved to the point where they have become one of the flagship trends in the Natural Language Processing (NLP) community, giving rise to various outlooks such as prompt-based, adapters or combinations with unsupervised approaches, among many others.
Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base
To evaluate the effectiveness of our approach, and due to the lack of datasets in this area, we also contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC.
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes.
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts.
A Systematic Review of Natural Language Processing Applied to Radiology Reports
Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited.
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
LE initialisation consistently boosted most deep learning models for automated medical coding.
Ranked #1 on
Multi-Label Text Classification
on MIMIC-III
