REVIEW 3 cited by
Few-shot Name Entity Recognition on StackOverflow
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the baseline. We improved the results further domain-specific phrase processing enhance results.
Forward citations
Cited by 3 Pith papers
-
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
The authors report that a BiLSTM-CRF feature extractor combined with XGBoost and logistic regression outperforms several baseline models for diabetes risk prediction on a private Beijing health-check dataset.
-
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
A 3D point cloud anomaly detection method combining FPFH, multi-view ResNet18 features, and graph convolution reports slightly higher MVTec 3D-AD scores than prior work, but the claimed sparse-convolution and L1 contr...
-
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose
IE-PONet is a proposed C3D plus OpenPose plus Bayesian optimization pipeline claiming minor benchmark gains, with no reproducible evidence.
Discussion (0). Continue with ORCID to comment.