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Proceedings of Sixth International Congress on Information and Communication Technology pp 897–909Cite as

BERT-Based Tagging Method for Social Issues in Web Articles

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

Abstract

In recent years, public collaboration to address social challenges has become increasingly important for the sustainable development of local and global communities. When discussing how to tackle social challenges, it is essential to investigate relevant existing activities. This study aims to collect web articles on various social issues to support such discussions and research. For this purpose, we have developed an automatic tagging system for web articles on social issues and evaluated it using the Bidirectional Encoder Representations from Transformers model (BERT), Wikidata, and Wikipedia. Specifically, our method classifies each sentence in a web article into social issue tags using BERT, which was trained beforehand using the Japanese Wikipedia. We constructed a training corpus using Wikidata and Wikipedia and extracted candidate social issue tags from the “Social issues” class of Wikidata. Hierarchical tagging is now possible, thanks to the structure of Wikidata. The results of our evaluation showed that the proposed method obtained F1 scores of 0.94 for zeroth-level classifications, 0.85 for first-level classifications, and 0.85 or better for all second-level classifications.

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Acknowledgements

This work was supported by NEDO (JPNP20006), JST CREST (JPMJCR15E1, JPMJCR20D1), and JSPS KAKENHI (17K00461).

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Authors and Affiliations

  1. Nagoya Institute of Technology, Nagoya, Japan

    Tokutaka Hasegawa & Shun Shiramatsu

Authors
  1. Tokutaka Hasegawa
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  2. Shun Shiramatsu
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Corresponding author

Correspondence to Tokutaka Hasegawa .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Hasegawa, T., Shiramatsu, S. (2022). BERT-Based Tagging Method for Social Issues in Web Articles. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_82

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