Summary of Hybrid X-linker: Automated Data Generation and Extreme Multi-label Ranking For Biomedical Entity Linking, by Pedro Ruas et al.
Hybrid X-Linker: Automated Data Generation and Extreme Multi-label Ranking for Biomedical Entity Linking
by Pedro Ruas, Fernando Gallego, Francisco J. Veredas, Francisco M. Couto
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
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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 paper proposes a novel approach to generate large-scale training datasets for deep learning-based biomedical entity linking. Current methods rely on human-labeled data, which is costly and limited in size. The proposed hybrid X-Linker pipeline combines different modules to link disease and chemical entity mentions to concepts in MEDIC and CTD-Chemical vocabularies, respectively. X-Linker was evaluated on several biomedical datasets, achieving top-1 accuracies ranging from 0.7895 to 0.9511. The model demonstrated superior performance in three datasets, while SapBERT outperformed it in the remaining three. Both models rely solely on mention strings for operations. The source code and associated data are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to create more training data for a special type of computer program that links medical terms together. Right now, these programs need lots of human-labeled data to work well. But creating this data is expensive and limited in size. The authors propose a new method to generate more data automatically. They test their approach on several biomedical datasets and find it works well for some of them. This means that we can use computers to link medical terms without needing as much human help. |
Keywords
» Artificial intelligence » Deep learning » Entity linking
