We introduce in this paper a new approach to improve deep learningbased architectures for multi-label document classification. Dependencies between labels are an essential factor in the multi-label context. Our proposed strategy takes advantage of the knowledge extracted from label co-occurrences. The proposed method consists in adding a regularization term to the loss function used for training the model, in a way that incorporates the label similarities given by the label co-occurrences to encourage the model to jointly predict labels that are likely to co-occur, and and not consider labels that are rarely present with each other. This allows the neural model to better capture label dependencies. Our approach was evaluated on three datasets: the standard AAPD dataset, a corpus of scientific abstracts and Reuters-21578, a collection of news articles, and a newly proposed multi-label dataset called arXiv-ACM. Our method demonstrates improved performance, setting a new state-of-the-art on all three datasets. CCS CONCEPTS• Applied computing → Document metadata; Digital libraries and archives; • Information systems → Digital libraries and archives; Content analysis and feature selection; Document collection models; • Computing methodologies → Neural networks.
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