Learning Section Weights for Multi-Label Document Classification
Maziar Moradi Fard Paula Sorrolla Bayod Kiomars Motarjem Mohammad Alian Nejadi Saber Akhondi Camilo Thorne
Multi-label document classification is a traditional task in NLP. Compared to
single-label classification, each document can be assigned multiple classes.
This problem is crucially important in various domains, such as tagging
scientific articles. Documents are often structured into several sections such
as abstract and title. Current approaches treat different sections equally for
multi-label classification. We argue that this is not a realistic assumption,
leading to sub-optimal results. Instead, we propose a new method called
Learning Section Weights (LSW), leveraging the contribution of each distinct
section for multi-label classification. Via multiple feed-forward layers, LSW
learns to assign weights to each section of, and incorporate the weights in the
prediction. We demonstrate our approach on scientific articles. Experimental
results on public (arXiv) and private (Elsevier) datasets confirm the
superiority of LSW, compared to state-of-the-art multi-label document
classification methods. In particular, LSW achieves a 1.3% improvement in terms
of macro averaged F1-score while it achieves 1.3% in terms of macro averaged
recall on the publicly available arXiv dataset.