@inproceedings{li-etal-2024-scalable,
    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.",
}

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.
Li, Dan Yadav, Vikrant Zhu, Zi Long Fard, Maziar Moradi Afzal, Zubair Tsatsaronis, George
Scalable Patent Classification with Aggregated Multi-View Ranking