Effective Collaborative Representation Learning for Multilabel Text Categorization

With the booming of deep learning, massive attention has been paid to developing neural models for multilabel text categorization (MLTC). Most of the works concentrate on disclosing word-label relationship, while less attention is taken in exploiting global clues, particularly with the relationship of document-label. To address this limitation, we propose an effective collaborative representation learning (CRL) model in this article. CRL consists of a factorization component for generating shallow representations of documents and a neural component for deep text-encoding and classification. We have developed strategies for jointly training those two components, including an alternating-least-squares-based approach for factorizing the pointwise mutual information (PMI) matrix of label-document and multitask learning (MTL) strategy for the neural component. According to the experimental results on six data sets, CRL can explicitly take advantage of the relationship of document-label and achieve competitive classification performance in comparison with some state-of-the-art deep methods.