Summary of Scidmt: a Large-scale Corpus For Detecting Scientific Mentions, by Huitong Pan and Qi Zhang and Cornelia Caragea and Eduard Dragut and Longin Jan Latecki
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions
by Huitong Pan, Qi Zhang, Cornelia Caragea, Eduard Dragut, Longin Jan Latecki
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 We present SciDMT, an enhanced corpus for scientific mention detection, outperforming existing resources. The corpus contains annotated documents for datasets (D), methods (M), and tasks (T) from 48 thousand scientific articles with over 1.8 million weakly annotated mentions. This is the largest corpus for scientific entity mention detection, offering a robust benchmark for the research community. We demonstrate its utility through experiments with advanced deep learning architectures like SciBERT and GPT-3.5. Our findings establish performance baselines and highlight unresolved challenges in scientific mention detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having access to a huge library of science articles, where you can quickly find specific information. That’s what scientists are trying to achieve with a new tool called SciDMT. It helps computers understand scientific papers better by annotating important terms and concepts. This is useful for indexing papers, improving search results, and making scientific knowledge more accessible. We tested this tool with advanced computer models and found that it works well, but there’s still room for improvement. |
Keywords
» Artificial intelligence » Deep learning » Gpt
