参考文献：

Rahul Agrawal, Archit Gupta, Yashoteja Prabhu, and Manik Varma. 2013. Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages. In Proceedings of the 22nd international conference on World Wide Web, pages 13–24.

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828.

Francisco Charte, Antonio J Rivera, María J del Jesus,and Francisco Herrera. 2015. Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing, 163:3–16.

Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel Weld. 2020. SPECTER: Document-level representation learning using citation-informed transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2270–2282, Online. Association for Computational Linguistics.

NCBI Resource Coordinators. 2017. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 46(D1):D8–D13.

Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019. Class-balanced loss based on effective number of samples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9260–9269.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

T. Durand, N. Mehrasa, and G. Mori. 2019. Learning a deep convnet for multi-label classification with partial labels. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 647–657, Los Alamitos, CA, USA. IEEE Computer Society.

Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz. 2004. A multiple resampling method for learning from imbalanced data sets. Computational intelligence, 20(1):18–36.

Weifeng Ge, Sibei Yang, and Yizhou Yu. 2018. Multievidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 

Philip J. Hayes and Steven P. Weinstein. 1990. Construe/tis: A system for content-based indexing of a database of news stories. In Proceedings of the The Second Conference on Innovative Applications of Artificial Intelligence, IAAI ’90, page 49–64. AAAI Press.

Gakuto Kurata, Bing Xiang, and Bowen Zhou. 2016. Improved neural network-based multi-label classification with better initialization leveraging label cooccurrence. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 521–526.

Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics.

Jianqiang Li, Guanghui Fu, Yueda Chen, Pengzhi Li, Bo Liu, Yan Pei, and Hui Feng. 2020a. A multilabel classification model for full slice brain computerised tomography image. BMC Bioinformatics, 21(6):200.

Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020b. Dice loss for dataimbalanced NLP tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 465–476, Online. Association for Computational Linguistics. 

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999–3007, Los Alamitos, CA, USA. IEEE Computer Society.

Zachary C. Lipton, Charles Elkan, and Balakrishnan Naryanaswamy. 2014. Optimal thresholding of classifiers to maximize f1 measure. In Machine Learning and Knowledge Discovery in Databases, pages 225–239, Berlin, Heidelberg. Springer Berlin Heidelberg. Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV), pages 565–571.

Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J Kim, and Johannes Fürnkranz. 2017. Maximizing subset accuracy with recurrent neural networks in multilabel classification. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.

Ankit Pal, Muru Selvakumar, and Malaikannan Sankarasubbu. 2020. Magnet: Multi-label text classification using attention-based graph neural network. In ICAART (2), pages 494–505. 

F. Pedregosa, G. Varoqu

aux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Yashoteja Prabhu, Anil Kag, Shrutendra Harsola, Rahul Agrawal, and Manik Varma. 2018. Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising. In Proceedings of the 2018 World Wide Web Conference, pages 993–1002.

Che-Ping Tsai and Hung-yi Lee. 2020. Order-free learning alleviating exposure bias in multi-label classification. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty- Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 6038–6045. AAAI Press.

George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, Yannis Almirantis, John Pavlopoulos, Nicolas Baskiotis, Patrick Gallinari, Thierry Artieres, Axel Ngonga, Norman Heino, Eric Gaussier, Liliana Barrio-Alvers, Michael Schroeder, Ion Androutsopoulos, and Georgios Paliouras. 2015. An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC Bioinformatics, 16:138.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics.

Jiawei Wu, Wenhan Xiong, and William Yang Wang. 2019. Learning to learn and predict: A metalearning approach for multi-label classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4354– 4364, Hong Kong, China. Association for Computational Linguistics.

Tong Wu, Qingqiu Huang, Ziwei Liu, Yu Wang, and Dahua Lin. 2020. Distribution-balanced loss for multi-label classification in long-tailed datasets. In Computer Vision – ECCV 2020, pages 162–178, Cham. Springer International Publishing. 

Wenshuo Yang, Jiyi Li, Fumiyo Fukumoto, and Yanming Ye. 2020. HSCNN: A hybrid-Siamese convolutional neural network for extremely imbalanced multi-label text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6716–6722, Online. Association for Computational Linguistics.

Yiming Yang and Xin Liu. 1999. A re-examination of text categorization methods. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’99, page 42–49, New York, NY, USA. Association for Computing Machinery.