论文标题
关于实体匹配的问题及其在应收账款的自动结算中的应用
On the problem of entity matching and its application in automated settlement of receivables
论文作者
论文摘要
本文涵盖了非政府组织中应收款的自动结算。我们通过实体匹配技术解决问题。我们考虑将基本算法用于匹配初步排名的设置，然后我们应用几种新颖的方法来提高基本算法的匹配质量：分数后处理，级联模型和链模型。此处介绍的方法有助于在开放世界中的应收款，实体匹配和多标签分类的自动结算。我们对现实世界的运营数据进行评估，这些数据来自公司提供应收款作为服务的公司：拟议的方法将召回召回率从78％（基本模型）提高到> 90％，而精度为99％。
This paper covers automated settlement of receivables in non-governmental organizations. We tackle the problem with entity matching techniques. We consider setup, where base algorithm is used for preliminary ranking of matches, then we apply several novel methods to increase matching quality of base algorithm: score post processing, cascade model and chain model. The methods presented here contribute to automated settlement of receivables, entity matching and multilabel classification in open-world scenario. We evaluate our approach on real world operational data which come from company providing settlement of receivables as a service: proposed methods boost recall from 78% (base model) to >90% at precision 99%.
