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Found 5 papers, 3 papers with code
Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges
However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.
Deep Span Representations for Named Entity Recognition
However, this typically results in significant ineffectiveness for long-span entities, a coupling between the representations of overlapping spans, and ultimately a performance degradation.
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions
In the past five years, several works have tailored for unsupervised representations of CT lesions via two-dimensional (2D) and three-dimensional (3D) self-supervised learning (SSL) algorithms.
Boundary Smoothing for Named Entity Recognition
Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration.
Ranked #2 on
Chinese Named Entity Recognition
on Weibo NER
Chinese Named Entity Recognition
named-entity-recognition
+3
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text
The resulted annotated corpus includes 1, 200 full medical records (or 18, 039 broken-down documents), and achieves inter-annotator agreements (IAAs) of 94. 53%, 73. 73% and 91. 98% F 1 scores for the three tasks.
