Multi-label Patent Classification using Attention-Aware Deep Learning Model
| Autor: | Wookey Lee, Jafar Afshar, Arousha Haghighian Roudsari, Charles Cheolgi Lee |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Data modeling Task (project management) Patent analysis 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Patent classification 0105 earth and related environmental sciences |
| Zdroj: | BigComp |
| DOI: | 10.1109/bigcomp48618.2020.000-2 |
| Popis: | Patent classification is challenging and essential for any further patent analysis task. We tackle the classification task on lower level patent classification (subgroup level) by using AttentionXML. Recently, pretraining methods for Natural Language Processing (NLP), such as DistilBERT pre-trained model, have achieved state-of-the-art results on some NLP tasks such as text classification. In this work we focus on investigating the effect of applying DistilBERT pre-trained model and fine-tuning it for the important task of multi-label patent classification. Moreover, the large USPTO-3M dataset (3,050,625 patents) based on CPC subclass and subgroup level is used for the purpose of comparing previous deep learning related studies. |
| Databáze: | OpenAIRE |
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