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.- Anthology ID:
- 2022.emnlp-main.610
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8923–8937
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.610
- DOI:
- 10.18653/v1/2022.emnlp-main.610
- Cite (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.
- Cite (Informal):
- Hierarchical Multi-Label Classification of Scientific Documents (Sadat & Caragea, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.emnlp-main.610.pdf