Results Merging in the Patent Domain

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). The models are tested in cooperative and uncooperative environments where text statistics and document scores from remote patent collections may be available or not respectively. We use two different methods for results merging, the multiple models (MMs) method, and the global models (GMs) method. 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.