Named Entity Recognition for Classifying Technoscientific Persons: Combining Pre-trained Language Models and Silver Standard Datasets

Author:

Süerdem Ahmet K.,Gümüş Samet

Publisher

Springer Nature Switzerland

Reference22 articles.

1. Hemlata Shelar, Gagandeep Kaur, Neha Heda & Poorva Agrawal (2020) Named Entity Recognition Approaches and Their Comparison for Custom NER Model, Science & Technology Libraries, 39:3, 324–337, https://doi.org/10.1080/0194262X.2020.1759479

2. Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F., & Vaithyanathan, S. (2010). Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks. Conference on Empirical Methods in Natural Language Processing.

3. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., … Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.

4. International Labour Organization. (2008). International Standard Classification of Occupations ISCO-08. Retrieved from https://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm

5. Neresini, F., & Lorenzet, A. (2016). Can media monitoring be a proxy for public opinion about technoscientific controversies? The case of the Italian public debate on nuclear power. Public Understanding of Science, 25(2), 171–185. https://doi.org/10.1177/0963662514551506

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