Classifying Patent Applications with Ensemble Methods
This work addresses the need for efficient patent classification for organizations like patent offices, though it is incremental as it applies existing ensemble methods to a specific dataset.
The authors tackled the problem of automatically classifying patent applications into eight coarse-grained categories using computational methods, achieving a top-ranked micro-averaged F1-Score of 0.778 in a competition with 14 teams.
We present methods for the automatic classification of patent applications using an annotated dataset provided by the organizers of the ALTA 2018 shared task - Classifying Patent Applications. The goal of the task is to use computational methods to categorize patent applications according to a coarse-grained taxonomy of eight classes based on the International Patent Classification (IPC). We tested a variety of approaches for this task and the best results, 0.778 micro-averaged F1-Score, were achieved by SVM ensembles using a combination of words and characters as features. Our team, BMZ, was ranked first among 14 teams in the competition.