DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
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| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
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| Challenge: | a new study examines the performance of event extractors to new domains without labeled data . event extraction is a key sub-task of interest for text understanding pipelines in multiple domains . |
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
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| Challenge: | a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes. |
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| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
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| Challenge: | Existing work evaluates event argument extraction with exact match (EM), where predicted arguments must align exactly with annotated spans. |
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