Summary of Bioncere: Non-contrastive Enhancement For Relation Extraction in Biomedical Texts, by Farshad Noravesh
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts
by Farshad Noravesh
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 introduces a new training method called biological non-contrastive relation extraction (BioNCERE) for relation extraction in the biomedical domain, which leverages transfer learning and non-contrastive learning to reduce annotation costs. The proposed approach avoids class collapse and full or dimensional collapse, allowing it to predict relations without knowledge of named entities. BioNCERE uses a three-stage pipeline, freezing weights learned in previous stages and leveraging non-contrastive learning in the second stage. Experiments on SemMedDB achieve state-of-the-art performance on relation extraction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help computers understand relationships between medical terms without needing to label every single term. It’s like teaching a computer to recognize patterns in medical information, without making it rely too heavily on specific words or phrases. This approach is important because it could make it easier and cheaper to train computers to understand medical text, which can help doctors and researchers find new insights and discoveries. |
Keywords
» Artificial intelligence » Transfer learning
