Exploring the Impact of Negative Sampling on Patent Citation Recommendation - Télécom Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Exploring the Impact of Negative Sampling on Patent Citation Recommendation

Résumé

Due to the increasing number of patents being published every day, patent citation recommendations have become one of the challenging tasks. Since patent citations may lead to legal and economic consequences, patent recommendations are even more challenging as compared to scientific article citations. One of the crucial components of the patent citation algorithm is negative sampling which is also a part of many other tasks such as text classification, knowledge graph completion, etc. This paper, particularly focuses on proposing a transformer-based ranking model for patent recommendations. It further experimentally compares the performance of patent recommendations based on various state-of-the-art negative sampling approaches to measure and compare the effectiveness of these approaches to aid future developments. These experiments are performed on a newly collected dataset of US patents from Google patents.
Fichier principal
Vignette du fichier
_PatentSemTech23_Citation_Recommendation.pdf (216.93 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04197089 , version 1 (05-09-2023)

Identifiants

Citer

Rima Dessi, Hidir Aras, Mehwish Alam. Exploring the Impact of Negative Sampling on Patent Citation Recommendation. Proceedings of the 4th Workshop on Patent Text Mining and Semantic Technologies co-located with SIGIR 2023, Jul 2023, Taipei, Taiwan. ⟨10.5281/zenodo.7870197⟩. ⟨hal-04197089⟩
94 Consultations
32 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More