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        <title>Hierarchical Multi-Label Classification of Scientific Documents</title>
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    <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 1,234 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 for all experiments publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.610</identifier>
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            <start>8923</start>
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%0 Conference Proceedings
%T Hierarchical Multi-Label Classification of Scientific Documents
%A Sadat, Mobashir
%A Caragea, Cornelia
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sadat-caragea-2022-hierarchical
%X 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 1,234 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 for all experiments publicly available.
%R 10.18653/v1/2022.emnlp-main.610
%U https://aclanthology.org/2022.emnlp-main.610
%U https://doi.org/10.18653/v1/2022.emnlp-main.610
%P 8923-8937

Markdown (Informal)

[Hierarchical Multi-Label Classification of Scientific Documents](https://aclanthology.org/2022.emnlp-main.610) (Sadat & Caragea, EMNLP 2022)

• Hierarchical Multi-Label Classification of Scientific Documents (Sadat & Caragea, EMNLP 2022)

ACL

• Mobashir Sadat and Cornelia Caragea. 2022. Hierarchical Multi-Label Classification of Scientific Documents. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8923–8937, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.