Summary of How to Evaluate Entity Resolution Systems: An Entity-centric Framework with Application to Inventor Name Disambiguation, by Olivier Binette et al.
How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
by Olivier Binette, Youngsoo Baek, Siddharth Engineer, Christina Jones, Abel Dasylva, Jerome P. Reiter
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 proposed entity resolution system evaluation method enables the creation of representative and reusable benchmark data sets, eliminating the need for complex sampling schemes. This framework facilitates model training and various evaluation tasks by integrating an entity-centric data labeling methodology with a unified monitoring system that tracks summary statistics, performance metrics, and error analysis. The validation is demonstrated through application to inventor name disambiguation and simulation studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the challenging problem of evaluating entity resolution systems, which are often hard to evaluate because they need to find matching records in large datasets. Instead of using complex sampling schemes, this system proposes creating benchmark data sets that can be used for training models and evaluating their performance. The framework includes labeling data with entities, tracking statistics, estimating key metrics like precision and recall, and analyzing errors. This is demonstrated through a real-world application to disambiguating inventor names. |
Keywords
* Artificial intelligence * Data labeling * Precision * Recall * Tracking
