Classifying Patent Applications with Ensemble Methods
Computation and Language
2018-11-13 v1
Abstract
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.
Cite
@article{arxiv.1811.04695,
title = {Classifying Patent Applications with Ensemble Methods},
author = {Fernando Benites and Shervin Malmasi and Marcos Zampieri},
journal= {arXiv preprint arXiv:1811.04695},
year = {2018}
}
Comments
Proceedings of ALTA 2018