Research map / Learning theory and algorithms / Clustering
A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Dist-2 rather than Dist-1 similarity distributions at the same ε | Node-Ordering and Edge-Ordering · Synthetic ER (ε=\frac{1}{5}, \frac{1}{10}, \frac{1}{20}) | queries: Dist-1: 6121, 6879, 7398 (Node-Ordering); 7231, 8545, 9296 (Edge-Ordering) -> Dist-2: 1506, 1986, 2760 (Node-Ordering); | Helped |
| Use Edge-Ordering rather than Node-Ordering | Crowd-ER · Synthetic ER (Dist-1, ε=\frac{1}{3}, \frac{1}{4}, \frac{1}{5}, \frac{1}{10}, \frac{1}{20}) | queries: 5207 vs 6003; 5883 vs 7145; 6121 vs 7231; 6879 vs 8545; 7398 vs 9296 | Hurt |
| Use Edge-Ordering rather than Node-Ordering | Crowd-ER · Synthetic ER (Dist-2) | queries: 1277 vs 1506; 1296 vs 1986; 1626 vs 2760 | Helped |
| Use Edge-Ordering rather than Node-Ordering | Crowd-ER · Cora | queries: 23,243 -> 21,099 | Helped |
| Use the informative Cora similarity matrix to order queries | Edge-Ordering and Node-Ordering · Cora | recall: above $0.996$ recall in less than $4,000$ queries | Helped |
| Use Edge-Ordering rather than Node-Ordering | Crowd-ER · Synthetic ER (Dist-1, ε=\frac{1}{2}) | queries: 4475 vs 4460 | Helped |
Closest papers by meaning
- Multipartite Entity Resolution: Motivating a K-Tuple Perspective (Student Abstract) (AAAI 2024 · similarity 0.86)
- Crowdsourced Clustering: Querying Edges vs Triangles (NeurIPS 2016 · similarity 0.82)
- Clustering with Noisy Queries (NeurIPS 2017 · similarity 0.82)
- Efficient Online Crowdsourcing with Complex Annotations (AAAI 2024 · similarity 0.80)
- Improving Consistency in Retrieval Augmented Systems With Group Similarity Rewards (NeurIPS 2026 · similarity 0.80)
- Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth (NeurIPS 2025 · similarity 0.80)
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