Papers by Enwei Zhu

3 papers
Revisiting De-Identification of Electronic Medical Records: Evaluation of Within- and Cross-Hospital Generalization (2023.emnlp-main)

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Challenge: De-identification is a natural language processing task to detect and remove the protected health information (PHI) from electronic medical records (EMRs).
Approach: They propose a de-identification dataset comprising EMRs from three hospitals in China and use it to establish a benchmark for evaluating both within- and cross-hospital generalization.
Outcome: The proposed model with almost perfect within-hospital performance struggles when transferred across hospitals.
Boundary Smoothing for Named Entity Recognition (2022.acl-long)

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Challenge: Named entity recognition models often encounter over-confidence issues . boundary smoothing is a method that re-assigns entity probabilities from annotated spans to the surrounding ones .
Approach: They propose a method for regularizing entity probabilities from annotated spans to the surrounding ones.
Outcome: The proposed method achieves better than or competitive with previous state-of-the-art systems on well-known benchmarks.
Deep Span Representations for Named Entity Recognition (2023.findings-acl)

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Challenge: Existing span-based NER systems shallowly aggregate token representations to span representations, causing performance degradations.
Approach: They propose a method that shallowly aggregates token representations to span representations . DSpERT produces span representation of deep semantics with weight initialization .
Outcome: The proposed model performs better than or competitive with state-of-the-art systems on six NER benchmarks.

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