Summary of Entity Decomposition with Filtering: a Zero-shot Clinical Named Entity Recognition Framework, by Reza Averly and Xia Ning
Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework
by Reza Averly, Xia Ning
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the use of open large language models (LLMs) for clinical named entity recognition (NER). Unlike previous studies that focus on proprietary LLMs, this work examines how these open models perform when trained specifically for entity recognition. The initial experiment reveals significant performance differences for certain clinical entities, which can be alleviated by a simple exploitment of entity types. To address this issue, the authors introduce a novel framework called entity decomposition with filtering (EDF). This framework decomposes the entity recognition task into multiple retrievals of sub-types and then filters them. Experimental results demonstrate the efficacy of EDF and improvements across all metrics, models, datasets, and entity types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how open large language models can be used for clinical named entity recognition (NER). These models are trained to recognize important entities in medical texts. The study finds that some clinical entities are harder to recognize than others, but this problem can be fixed by using simple strategies. To solve this issue, the authors create a new way of recognizing entities called EDF (entity decomposition with filtering). This method breaks down the task into smaller steps and then filters out the results. The study shows that EDF works well and improves recognition for all types of entities. |
Keywords
* Artificial intelligence * Named entity recognition * Ner
