In order to capture inter-dependencies between types, we propose a hybrid model that incorporates latent type representation in addition to binary relevance.
the model learns to predict a low-dimensional vector that encodes latent type features obtained through Principle Label Space Transformation (Tai and Lin, 2012) and reconstruct the sparse and high-dimensional type vector from this latent representation.