标题： 通过微聚类进行大规模实体解析 Ewens--Pitman随机划分 显示英文标题

标题： Large-scale entity resolution via microclustering Ewens--Pitman random partitions

摘要： 我们引入了微聚类Ewens-Pitman模型，用于随机划分，该模型通过将Ewens-Pitman模型的强度参数与样本量线性缩放得到。 结果表明，这种随机划分具有微聚类特性，即：最大聚类的大小随着样本量的增长呈次线性增长，而聚类的数量则呈线性增长。 通过利用Ewens-Pitman随机划分与Pitman-Yor过程之间的相互作用，我们开发了高效的变分推断方案，用于实体解析中的后验计算。 我们的方法在现有贝叶斯方法中实现了三个数量级的速度提升，同时保持了具有竞争力的实证性能。 显示英文摘要

摘要： We introduce the microclustering Ewens--Pitman model for random partitions, obtained by scaling the strength parameter of the Ewens--Pitman model linearly with the sample size. The resulting random partition is shown to have the microclustering property, namely: the size of the largest cluster grows sub-linearly with the sample size, while the number of clusters grows linearly. By leveraging the interplay between the Ewens--Pitman random partition with the Pitman--Yor process, we develop efficient variational inference schemes for posterior computation in entity resolution. Our approach achieves a speed-up of three orders of magnitude over existing Bayesian methods for entity resolution, while maintaining competitive empirical performance.