Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
원문 초록
This paper introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks Office patent data exploration tool that disambiguates patent inventors using an entity resolution algorithm. We provide a data collection methodology and tailored performance estimators that account for sampling biases. Our approach is simple, practical and principled -- key characteristics that allow us to paint the first representative picture of PatentsView's disambiguation performance. This approach is used to inform PatentsView's users of the reliability of the data and to allow the comparison of competing disambiguation algorithms.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution
T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework
Fine-tuning Large Language Models for Entity Matching
Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching
Towards Universal Dense Blocking for Entity Resolution
How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.