Hierarchical Multi-Label Classification of Scientific Documents
Mobashir Sadat, Cornelia Caragea · 2022
Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection.With the large number of topics being available in recent years, it has become necessary to arrange them in a hierarchy.Therefore, the automatic classification systems need to be able to classify the documents hierarchically.In addition, each paper is often assigned to more than one relevant topic.For example, a paper can be assigned to several topics in a hierarchy tree.In this paper, we introduce a new dataset for hierarchical multi-label text classification (HMLTC) of scientific papers called SciHTC, which contains 186, 160 papers and 1, 233 categories from the ACM CCS tree.We establish strong baselines for HMLTC and propose a multi-task learning approach for topic classification with keyword labeling as an auxiliary task.Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities on hierarchical scientific topic classification.We make our dataset and code available on Github. 1