  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.
