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Found 5 papers, 3 papers with code
Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports
Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset.
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
We then show that they can be prevented in practice by introducing a Discrete Fourier Transform (DFT) output layer, which guarantees that all sparse label combinations with up to $k$ active labels are argmaxable.
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice
Classifiers in natural language processing (NLP) often have a large number of output classes.
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.
What do character-level models learn about morphology? The case of dependency parsing
When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology.
