@inproceedings{li-etal-2024-scalable-patent,
title = "Scalable Patent Classification with Aggregated Multi-View Ranking",
author = "Li, Dan and
Yadav, Vikrant and
Zhu, Zi Long and
Fard, Maziar Moradi and
Afzal, Zubair and
Tsatsaronis, George",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1249",
pages = "14336--14346",
abstract = "Automated patent classification typically involves assigning labels to a patent from a taxonomy, using multi-class multi-label classification models. However, classification-based models face challenges in scaling to large numbers of labels, struggle with generalizing to new labels, and fail to effectively utilize the rich information and multiple views of patents and labels. In this work, we propose a multi-view ranking-based method to address these limitations. Our method consists of four ranking-based models that incorporate different views of patents and a meta-model that aggregates and re-ranks the candidate labels given by the four ranking models. We compared our approach against the state-of-the-art baselines on two publicly available patent classification datasets, USPTO-2M and CLEF-IP-2011. We demonstrate that our approach can alleviate the aforementioned limitations and achieve a new state-of-the-art performance by a significant margin.",
}
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<abstract>Automated patent classification typically involves assigning labels to a patent from a taxonomy, using multi-class multi-label classification models. However, classification-based models face challenges in scaling to large numbers of labels, struggle with generalizing to new labels, and fail to effectively utilize the rich information and multiple views of patents and labels. In this work, we propose a multi-view ranking-based method to address these limitations. Our method consists of four ranking-based models that incorporate different views of patents and a meta-model that aggregates and re-ranks the candidate labels given by the four ranking models. We compared our approach against the state-of-the-art baselines on two publicly available patent classification datasets, USPTO-2M and CLEF-IP-2011. We demonstrate that our approach can alleviate the aforementioned limitations and achieve a new state-of-the-art performance by a significant margin.</abstract>
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%0 Conference Proceedings
%T Scalable Patent Classification with Aggregated Multi-View Ranking
%A Li, Dan
%A Yadav, Vikrant
%A Zhu, Zi Long
%A Fard, Maziar Moradi
%A Afzal, Zubair
%A Tsatsaronis, George
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F li-etal-2024-scalable-patent
%X Automated patent classification typically involves assigning labels to a patent from a taxonomy, using multi-class multi-label classification models. However, classification-based models face challenges in scaling to large numbers of labels, struggle with generalizing to new labels, and fail to effectively utilize the rich information and multiple views of patents and labels. In this work, we propose a multi-view ranking-based method to address these limitations. Our method consists of four ranking-based models that incorporate different views of patents and a meta-model that aggregates and re-ranks the candidate labels given by the four ranking models. We compared our approach against the state-of-the-art baselines on two publicly available patent classification datasets, USPTO-2M and CLEF-IP-2011. We demonstrate that our approach can alleviate the aforementioned limitations and achieve a new state-of-the-art performance by a significant margin.
%U https://aclanthology.org/2024.lrec-main.1249
%P 14336-14346
Markdown (Informal)
[Scalable Patent Classification with Aggregated Multi-View Ranking](https://aclanthology.org/2024.lrec-main.1249) (Li et al., LREC-COLING 2024)
ACL
- Dan Li, Vikrant Yadav, Zi Long Zhu, Maziar Moradi Fard, Zubair Afzal, and George Tsatsaronis. 2024. Scalable Patent Classification with Aggregated Multi-View Ranking. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14336–14346, Torino, Italia. ELRA and ICCL.