Papers by Jangwon Kim
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data (2022.acl-long)
Copied to clipboard
| Challenge: | Existing text classification systems require thousands of in-domain text data to achieve high performance. |
| Approach: | They propose an algorithm to improve task transferability of meta-learning-based text classification by normalizing negative conditional entropy from source task data to boost cross-domain meta- learning accuracy. |
| Outcome: | The proposed method improves section classification accuracy significantly compared to meta-learning algorithms. |
Domain-agnostic Question-Answering with Adversarial Training (D19-58)
Copied to clipboard
| Challenge: | Adapting models to new domain without finetuning is a challenging problem in deep learning. |
| Approach: | They propose an adversarial training framework for domain generalization in Question Answering task using a conventional QA model and a discriminator. |
| Outcome: | The proposed model outperforms the baseline model on Question Answering (QA) task. |
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification (2025.acl-industry)
Copied to clipboard
Praphul Singh, Charlotte Dzialo, Jangwon Kim, Sumana Srivatsa, Irfan Bulu, Sri Gadde, Krishnaram Kenthapadi
| Challenge: | Existing de-identification methods suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. |
| Approach: | They propose a multi-modal framework for de-identifying electronic health records using a retrieval-based entity relexicalization approach. |
| Outcome: | The proposed framework achieves competitive performance while optimizing token usage to reduce LLM costs. |