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/emnlp-22-attachments/U18-1012.pdf