Abstract
Under the background of different Bie-modernism, starting from the “different from” way of thinking, seeking original words and thoughts, and establishing true modern thoughts, in which deep fake and deep identification can be compared with the cultural calculations in machine learning. The combination of computing technology and its technical support can realize the modernity of authenticity recognition. This paper mainly uses characters identification as an example. Because characters will have many characteristics, this paper adopts a multi-dimensional consideration, including the appearance, actions, language, and other aspects of the characters for cultural calculations, so as to show that computers can be effectively combined with other modern times with strong feasibility and high efficiency. The experiment uses the combination of Word2Vec algorithm and Transformer network to form a cultural calculation model for character identification tasks. Experiments on the Chinese dataset show that this model can obtain deep features in the text, and still has a good screening effect for data with similar surface features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, J.: Bie-modernism: Beyond aesthetics and after postmodern–a reaction to an international aesthetic trend. J. Shanghai Norm. Univ. (Philos. Soc. Sci. Edn.) 44(01), 5–14 (2015)
Chen, K., Zhu, Y.: A review of machine learning and its related algorithms. Stat. Inf. Forum (05), 105–112 (2007)
Zhou, Y.: Introduction to machine learning and its related algorithms. Sci. Technol. Commun. 11(06), 153–154+165 (2019)
Zhao, H., Peng, H., Chen, H.: Online service platform for Chinese cultural genes. Comput. Syst. Appl. 12, 52–57 (2015)
Li, Y.: Research on Chinese automatic word separation based on HMM for single word valuation. Inner Mongolia Normal University (2010)
Deng, L., Luo, Z.Y.: Semi-supervised CRF-based cross-domain Chinese word separation. Chin. J. Inform. 31(4), 9–19 (2017)
Laaroussi, S., Si, L.A., Yousfi, A.: Distant n-gram language model for contextual spelling correction applied to arabic language (2021)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR (2013)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. arXiv (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Qi, Z., Chen, H., Liu, M., Wang, J. (2022). Bie—Modernism with Cultural Calculations in Multiple Dimensions. In: Rauterberg, M. (eds) Culture and Computing. HCII 2022. Lecture Notes in Computer Science, vol 13324. Springer, Cham. https://doi.org/10.1007/978-3-031-05434-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-05434-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05433-4
Online ISBN: 978-3-031-05434-1
eBook Packages: Computer ScienceComputer Science (R0)