Multi-label Text Classification of Economic Concepts from Economic News Articles using Natural Language Processing
- Authors
- Kim, S.; Lee, M.; Seok, J.
- Issue Date
- Jul-2022
- Publisher
- IEEE Computer Society
- Keywords
- Multi-label Classification; Natural Language Processing; Text Classification
- Citation
- International Conference on Ubiquitous and Future Networks, ICUFN, v.2022-July, pp 417 - 420
- Pages
- 4
- Journal Title
- International Conference on Ubiquitous and Future Networks, ICUFN
- Volume
- 2022-July
- Start Page
- 417
- End Page
- 420
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69897
- DOI
- 10.1109/ICUFN55119.2022.9829557
- ISSN
- 2165-8528
2165-8536
- Abstract
- Multi-label classification is rapidly developing as an important aspect of modern predictive modeling. In this paper, we propose a multi-label text classification approach in order to extract the labels of economic concepts from economic news articles. We demonstrate a multi-label sentence-level event classification with a multi-label classifier algorithm. The classifier uses BERT Model and classification based on the association between labels via a threshold. The experiment on real-world multi-label data with many labels demonstrates an appealing performance and efficiency of multi-label classification.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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