metadata

language:
  - fr
license:
  - cc-by-nc-sa-3.0
multilinguality:
  - monolingual
task_categories:
  - text-classification, multi-label-classification

Dataset Card for IPC classification of French patents

Dataset Description

Homepage:
Repository: IPC Classification of French Patents
Paper: Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents
Point of Contact: You Zuo

Dataset Summary

INPI-CLS is a French Patents corpus extracted from the internal database of the INPI (National Institute of Industrial Property of France). It was initially designed for the patent classification task and consists of approximately 296k patent texts (including title, abstract, claims, and description) published between 2002 and 2021. Each patent in the corpus is annotated with labels ranging from sections to the IPC subgroup levels.

Languages

French

Domain

Patents (intellectual property).

Social Impact of Dataset

The purpose of this dataset is to help develop models that enable the classification of French patents in the International Patent Classification (IPC) system standard.

Thanks to the high integrity of the data, the INPI-CLS corpus can be utilized for various analytical studies concerning French language patents. Moreover, it serves as a valuable resource as a scientific corpus that comprehensively documents the technological inventions of the country.

Citation Information

@inproceedings{zuo:hal-03850405,
  TITLE = {{Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents}},
  AUTHOR = {Zuo, You and Mouzoun, Houda and Ghamri Doudane, Samir and Gerdes, Kim and Sagot, Beno{\^i}t},
  URL = {https://hal.archives-ouvertes.fr/hal-03850405},
  BOOKTITLE = {{SIGIR 2022 - PatentSemTech workshop}},
  ADDRESS = {Madrid, Spain},
  YEAR = {2022},
  MONTH = Jul,
  KEYWORDS = {IPC prediction ; Clustering and Classification ; Extreme Multi-label Learning ; French ; Patent},
  PDF = {https://hal.archives-ouvertes.fr/hal-03850405/file/PatentSemTech_2022___extended_abstract.pdf},
  HAL_ID = {hal-03850405},
  HAL_VERSION = {v1},
}