Hierarchical Multi-Label Classification of Scientific Documents

• URL: http://arxiv.org/abs/2211.02810v1
• Date: Sat, 5 Nov 2022 04:12:57 GMT
• Title: Hierarchical Multi-Label Classification of Scientific Documents
• Authors: Mobashir Sadat, Cornelia Caragea
• Abstract summary: We introduce a new dataset for hierarchical multi-label text classification of scientific papers called SciHTC. This dataset contains 186,160 papers and 1,233 categories from the ACM CCS tree. Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities.
• Score: 47.293189105900524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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