Summary of Biomedical Entity Linking As Multiple Choice Question Answering, by Zhenxi Lin et al.
Biomedical Entity Linking as Multiple Choice Question Answering
by Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a novel approach to biomedical entity linking (BioEL) called BioELQA, which treats BioEL as multiple-choice question answering. The model first retrieves candidate entities using a fast retriever and then jointly presents the mention and candidate entities to a generator. This formulation allows for explicit comparison of different candidate entities, capturing fine-grained interactions between mentions and entities. To improve generalization on long-tailed entities, the authors retrieve similar labeled training instances as clues and concatenate them with the input for the generator. BioELQA outperforms state-of-the-art baselines on several datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to connect biomedical information (like disease names) to their definitions in medical texts. The problem is that current methods aren’t good at finding rare or specific connections between these terms. To fix this, the authors created a model called BioELQA that works like a multiple-choice quiz. It looks for possible answers and compares them to find the best one. This helps it understand the relationships between different biomedical terms. The new approach does better than other methods on several tests. |
Keywords
* Artificial intelligence * Entity linking * Generalization * Question answering
