Abstract
Named entity recognition has a variety of applications in journalism, where it may extract relevant information from voluminous daily news reports. However, its applicability is limited since there is no word vector learning and existing models are complicated. This paper is based on the lightweight Natural Language Processing model (ALBERT) dynamic word vector generation model proposed by Google. The model was combined with Bidirectional Long Short-Term Memory Network (LSTM) and Conditional Random Field (CRF) to form the ALBERT-BiLSTM-CRF model. This paper applies the ALBERT-BiLSTM-CRF model with the 2014 edition of the People’s Daily published on the Internet as the primary data set to compare the traditional statistical model and the classic NLP model. The experimental results show that the ALBERT-BiLSTM-CRF has a comparative advantage over the classic natural language processing (NLP) model. The proposed model can increase the recognition accuracy and recall rate of named entities in the news. The model's accuracy and recall on the test dataset attained 94.49 and 89.50 percent, respectively, while the model's volume allows for lightweight deployment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elman, J.L.: Finding structure in time. Cogn. Sci. 14, 179–211 (1990)
Luo, L.: An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics 34, 1381–1388 (2018)
Ali, M.N.A., Tan, G., Hussain, A.: Boosting Arabic named-entity recognition with multi-attention layer. IEEE Access. 7, 46575–46582 (2019)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Sun, W., Chen, T.: Research on Microblog rumor recognition method based on ALBERT-Bi LSTM model. Computer Era. 21–26 (2020)
Rau, L.F.: Extracting company names from text. In: Proceedings the Seventh IEEE Conference on Artificial Intelligence Application. pp. 29–30. IEEE Computer Society (1991)
Cheung, L., Tsou, B.K., Sun, M.: Identification of chinese personal names in unrestricted texts. In: Proceedings of the 16th Pacific Asia Conference on Language, Information and Computation. pp. 28–35 (2001)
McCallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. (2003)
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. arXiv preprint arXiv:1805.02023 (2018)
Zhang, D., Chen, W.: Chinese named entity recognition based on contextualized char embeddings. Comput. Sci. 48, 233–238
Borthwick, A.E.: A maximum entropy approach to named entity recognition. New York University (1999)
Bikel, D.M., Schwartz, R., Weischedel, R.M.: An algorithm that learns what’s in a name. Mach. Learn. 34, 211–231 (1999)
Guanming, Z., Chuang, Z., Bo, X., Zhiqing, L.: CRFs-based Chinese named entity recognition with improved tag set. In: 2009 WRI World Congress on Computer Science and Information Engineering. pp. 519–522. IEEE (2009)
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Chinese Institute of Command and Control
About this paper
Cite this paper
Ren, K., Li, H., Zeng, Y., Zhang, Y. (2022). Named Entity Recognition with CRF Based on ALBERT: A Natural Language Processing Model. In: Proceedings of 2022 10th China Conference on Command and Control. C2 2022. Lecture Notes in Electrical Engineering, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_45
Download citation
DOI: https://doi.org/10.1007/978-981-19-6052-9_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6051-2
Online ISBN: 978-981-19-6052-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)