论文标题
结果合并在专利领域
Results Merging in the Patent Domain
论文作者
论文摘要
在本文中，我们测试了机器学习方法以合并专利文档检索的结果。具体而言，我们检查随机森林，决策树，支持向量机（SVR），线性回归，多项式回归和深神经网络（DNNS）。我们使用两种不同的方法进行合并，多个模型（MM）方法和全局模型方法（GM）。此外，我们检查文档分数的排名是否可以线性解释。我们的实验中使用了CLEF-IP 2011标准测试收集。与所有其他模型相比，随机森林可产生最佳结果，并且比线性和多项式方法更适合数据。
In this paper, we test machine learning methods for results merging in patent document retrieval. Specifically, we examine random forest, decision tree, support vector machine (SVR), linear regression, polynomial regression, and deep neural networks (DNNs). We use two different methods for results merging, the multiple models (MM) method and the global model method (GM). Furthermore, we examine whether the ranking of the document's scores is linearly explainable. The CLEF-IP 2011 standard test collection was used in our experiments. The random forest produces the best results in comparison to all other models, and it fits the data better than linear and polynomial approaches.
