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Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set.
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Prabhu Y, Varma M. Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proc. of ACM SIGKDD, 2014: 263-272
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Bhatia K, Jain H, Kar P, Varma M, Jain P. Sparse local embeddings for extreme multi-label classification. In: Proc. of NIPS. 2015: 730-738
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Liu J, Chang C, Wu Y, Yang Y. Deep learning for extreme multi-label text classification. In: Proc. of the 40th ACM SIGIR, 2017: 115-124
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Munkhdalai T, Yu H. Neural semantic encoders. In: Proc. of ACL, 2017, 1: 397
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Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. In: Proc. of NIPS. 2015: 649-657
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Jain H, Prabhu Y, Varma M. Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In: Proc. of ACM SIGKDD, 2016: 935-944
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2018
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Prabhu Y, Kag A, Harsola S, Agrawal R, Varma M. Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising. In: Proc. of WWW, 2018: 993-1002
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Zhang W, Yan J, Wang X, Zha H. Deep extreme multi-label learning. In: Proc. of ACM ICMR, 2018: 100-107
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