@inproceedings{benites-etal-2018-classifying,
    title = "Classifying Patent Applications with Ensemble Methods",
    author = "Benites, Fernando  and
      Malmasi, Shervin  and
      Zampieri, Marcos",
    editor = "Kim, Sunghwan Mac  and
      Zhang, Xiuzhen (Jenny)",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/U18-1012/",
    pages = "89--92",
    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."
}