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Conference Papers Year : 2023

Exploring Data-Centric Strategies for French Patent Classification: A Baseline and Comparisons

Abstract

This paper proposes a novel approach to French patent classification leveraging data-centric strategies. We compare different approaches for the two deepest levels of the IPC hierarchy: the IPC group and subgroups. Our experiments show that while simple ensemble strategies work for shallower levels, deeper levels require more sophisticated techniques such as data augmentation, clustering, and negative sampling. Our research highlights the importance of language-specific features and data-centric strategies for accurate and reliable French patent classification. It provides valuable insights and solutions for researchers and practitioners in the field of patent classification, advancing research in French patent classification.
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Dates and versions

hal-04130188 , version 1 (20-06-2023)

Identifiers

  • HAL Id : hal-04130188 , version 1

Cite

You Zuo, Kim Gerdes, Houda Mouzoun, Samir Ghamri Doudane, Benoît Sagot. Exploring Data-Centric Strategies for French Patent Classification: A Baseline and Comparisons. 18e Conférence en Recherche d'Information et Applications -- 16e Rencontres Jeunes Chercheurs en RI -- 30e Conférence sur le Traitement Automatique des Langues Naturelles -- 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues, Jun 2023, Paris, France. pp.349-365. ⟨hal-04130188⟩
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