Summary of Cycle: Cross-year Contrastive Learning in Entity-linking, by Pengyu Zhang et al.
CYCLE: Cross-Year Contrastive Learning in Entity-Linking
by Pengyu Zhang, Congfeng Cao, Klim Zaporojets, Paul Groth
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a novel approach to address the issue of temporal degradation in entity linking models. Entity graphs constantly evolve with new entities emerging and relationships changing, causing existing models’ performance to decline over time. The proposed method, CYCLE (Cross-Year Contrastive Learning for Entity-Linking), leverages graph relationships to aggregate information from neighboring entities across different time periods, enhancing the ability to distinguish similar entities over time. This contrastive learning method treats newly added graph relationships as positive samples and removed ones as negative samples, helping prevent temporal degradation and achieve a 13.90% performance improvement over the state-of-the-art for one-year time gaps, and a 17.79% improvement for three-year gaps. The model shows robustness to low-degree entities, which are less resistant to temporal degradation due to their sparse connectivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big map with lots of information about people, places, and things. Over time, new details might be added or old ones removed, making it harder for computers to understand the relationships between them. This paper finds a way to help computers keep up by looking at how things are connected across different times. It’s like taking a snapshot of the map at multiple points in time and comparing them to see what’s changed. This helps prevent mistakes when trying to find specific information, making it more accurate and reliable. |
Keywords
» Artificial intelligence » Entity linking
