Classifying Patent Applications with Ensemble Methods
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.- Anthology ID:
- U18-1012
- Volume:
- Proceedings of the Australasian Language Technology Association Workshop 2018
- Month:
- December
- Year:
- 2018
- Address:
- Dunedin, New Zealand
- Editors:
- Sunghwan Mac Kim, Xiuzhen (Jenny) Zhang
- Venue:
- ALTA
- SIG:
- Publisher:
- Note:
- Pages:
- 89–92
- Language:
- URL:
- https://aclanthology.org/U18-1012
- DOI:
- Cite (ACL):
- Fernando Benites, Shervin Malmasi, and Marcos Zampieri. 2018. Classifying Patent Applications with Ensemble Methods. In Proceedings of the Australasian Language Technology Association Workshop 2018, pages 89–92, Dunedin, New Zealand.
- Cite (Informal):
- Classifying Patent Applications with Ensemble Methods (Benites et al., ALTA 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/U18-1012.pdf