Hierarchical Multi-Label Classification of Scientific Documents
This addresses the problem of managing and indexing scientific documents with hierarchical topics for researchers and digital libraries, but it is incremental as it builds on existing classification methods with a new dataset.
The authors tackled hierarchical multi-label classification of scientific papers by introducing a new dataset, SciHTC, with 186,160 papers and 1,233 categories, and proposed a multi-task learning approach that achieved a Macro-F1 score of 34.57%.
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.