Optimizing Neural Networks for Patent Classification
Abstract
A great number of patents is filed everyday to the patent offices worldwide. Each of these patents has to be labeled by domain experts with one or many of thousands of categories. This process is not only extremely expensive but also overwhelming for the experts, due to the considerable increase of filed patents over the years and the increasing complexity of the hierarchical categorization structure. Therefore, it is critical to automate the manual classification process using a classification model. In this paper, the automation of the task is carried out based on recent advances in deep learning for NLP and compared to customized approaches. Moreover, an extensive optimization analysis grants insights about hyperparameter importance. Our optimized convolutional neural network achieves a new state-of-the-art performance of $55.02\%$ accuracy on the public Wipo-Alpha dataset.
Cite
Text
Abdelgawad et al. "Optimizing Neural Networks for Patent Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_41Markdown
[Abdelgawad et al. "Optimizing Neural Networks for Patent Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/abdelgawad2019ecmlpkdd-optimizing/) doi:10.1007/978-3-030-46133-1_41BibTeX
@inproceedings{abdelgawad2019ecmlpkdd-optimizing,
title = {{Optimizing Neural Networks for Patent Classification}},
author = {Abdelgawad, Louay and Kluegl, Peter and Genc, Erdan and Falkner, Stefan and Hutter, Frank},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {688-703},
doi = {10.1007/978-3-030-46133-1_41},
url = {https://mlanthology.org/ecmlpkdd/2019/abdelgawad2019ecmlpkdd-optimizing/}
}