@inproceedings{ada59b0b7dd24bf69c69257ee959a849,
title = "ok Etiketli Hiyerarsik CPC Siniflandirmasinda Farkli Yaklasimlarin Kiyaslanmasi",
abstract = "Accurate and consistent classification of patents is crucial for intellectual property management, analyzing technology trends and improving patent search processes. The Cooperative Patent Classification (CPC) system provides a multi-label and hierarchical structure, allowing patents to be organized according to specific technical fields. However, due to this complex structure, manual classification processes become both time-consuming and error-prone. In this paper, four different deep learning-based approaches for CPC classification are compared: (i) flat (non-hierarchical) classification, (ii) hierarchical multitask learning, (iii) hierarchical loss function, and (iv) classification using semantic similarity via label embeddings.",
keywords = "cpc classification, hierarchical classification, multi-label classification",
author = "Taskin, \{Emre Doruk\} and Baran Kilic and Ferayenur Bozkurt and Gecin, \{Irfan Ulas\} and Ozcan Somuncu and Sahin, \{Pinar Duygulu\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 ; Conference date: 25-06-2025 Through 28-06-2025",
year = "2025",
doi = "10.1109/SIU66497.2025.11111861",
language = "T{\"u}rk{\c c}e",
series = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",
address = "!!United States",
}