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Leveraging Distributional Semantics for Multi-Label Learning
[article]
2017
arXiv
pre-print
We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings for natural language processing tasks. Learning such embeddings can be reduced to a certain matrix factorization. Our approach is novel in that it
arXiv:1709.05976v3
fatcat:4idg7s27ivaadn443jponx5tfi