Entity Resolution, also called record linkage or deduplication, refers to the process of identifying and merging duplicate versions of the same entity into a unified representation. The standard practice is to use a Rule based or Machine Learning based model that compares entity pairs and assigns a score to represent the pairs' Match/Non-Match status. However, performing an exhaustive pair-wise comparison on all pairs of records leads to quadratic matcher complexity and hence a Blocking step is performed before the Matching to group similar entities into smaller blocks that the matcher can then examine exhaustively. Several blocking schemes have been developed to efficiently and effectively block the input dataset into manageable groups. At CareerBuilder (CB), we perform deduplication on massive datasets of people profiles collected from disparate sources with varying informational content. We observed that, employing a single blocking technique did not cover the base for all possible scenarios due to the multi-faceted nature of our data sources. In this paper, we describe our ensemble approach to blocking that combines two different blocking techniques to leverage their respective strengths.
Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability
Department of Atmospheric and Climate Science, University of Washington（大气与气候科学系，华盛顿大学）；Department of Geosciences, Princeton University（地球科学系，普林斯顿大学）；High Meadows Environmental Institute, Princeton University（高梅斯环境研究所，普林斯顿大学）
Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；Mayo Clinic（梅奥诊所）；School of Medicine, Yale University（耶鲁大学医学院）
EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature
National Centre for Text Mining（国家文本挖掘中心）；Manchester Centre for Genomic Medicine（曼彻斯特基因组医学中心）；St Mary’s Hospital（圣玛丽医院）；The University of Manchester（曼彻斯特大学）；Manchester University Hospitals NHS Foundation Trust（曼彻斯特大学医院 NHS 基金会信托）；School of Biological Sciences（生物科学学院）；Faculty of Biology, Medicine and Health（生物、医学与健康学部）；School of Medical Sciences（医学科学学院）；Christie NHS Foundation Trust（克里斯蒂 NHS 基金会信托）
