A Study of the Combination of Semantic Understanding Enhancement Methods and Deep Learning Techniques in English Translation

[1] Zhao, X., & Jiang, Y. (2022). Synchronously improving multi-user english translation ability by using ai. International Journal on Artificial Intelligence Tools. Search in Google Scholar

[2] Berdejo-Espinola, V., & Amano, T. (2023). Al tools can improve equity in science. Science. Search in Google Scholar

[3] Wang, R. (2021). Research on intelligent english translation method based on the improved attention mechanism model. Scientific Programming. Search in Google Scholar

[4] Li, J. (2021). Design, implementation, and evaluation of online english learning platforms. Wireless Communications and Mobile Computing, 2021(1), 1-11. Search in Google Scholar

[5] Tang, N., Li, B., Tsai, S. B., & Sun, G. (2021). A practical exploration of constructive english learning platform informatization based on rbf algorithm. Mathematical Problems in Engineering, 2021. Search in Google Scholar

[6] Sun, Z., Anbarasan, M., & Kumar, D. P. (2020). Design of online intelligent english teaching platform based on artificial intelligence techniques. Computational Intelligence. Search in Google Scholar

[7] Winter, B., Fischer, M. H., Scheepers, C., & Myachykov, A. (2023). More is better: english language statistics are biased toward addition. Cognitive Science. Search in Google Scholar

[8] Sadeghi, K., & Pourbahram, R. (2024). Exploring factors affecting english language teacher wellbeing: insights from positive psychology. System, 122. Search in Google Scholar

[9] Xin, Y. P., Tzur, R., Driver, M. K., & Powell, S. R. (2017). Culturally and linguistically responsive schema intervention:improving word problem solving for english language learners with mathematics difficulty. Learning Disability Quarterly, 40(1), 41-53. Search in Google Scholar

[10] Armaselu, F., Apostol, E., Khan, A. F., Liebeskind, C., Mcgillivray, B., & Truic, C. O., et al. (2022). Ll(o)d and nlp perspectives on semantic change for humanities research. Semantic Web, 13, 1051-1080. Search in Google Scholar

[11] Hames, Harvey, Cohen, Yochai, Neuman, & Yair. (2017). An information-based procedure for measuring semantic change in historical data. Measurement. Search in Google Scholar

[12] Ajienka, N., Capiluppi, A., & Counsell, S. (2018). An empirical study on the interplay between semantic coupling and co-change of software classes. Empirical Software Engineering, 23(3), 1791-1825. Search in Google Scholar

[13] Prasad, J. V. D., Sreelatha, M., & Suvarnavani, K. (2023). Semantic land cover change detection using hardnet and dual path coronet. International journal of remote sensing(23/24), 44. Search in Google Scholar

[14] Deng, D., & Xue, N. (2017). Translation divergences in chinese–english machine translation: an empirical investigation. Computational Linguistics, 1-65. Search in Google Scholar

[15] YangJuan. (2021). Digital mining algorithm of english translation course information based on digital twin technology. Wireless Communications and Mobile Computing. Search in Google Scholar

[16] Zhang, T. (2022). Deep learning classification model for english translation styles introducing attention mechanism. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[17] Zhon, Y., Wu, S., & Zhao, B. (2017). Scene semantic understanding based on the spatial context relations of multiple objects. Remote Sensing. Search in Google Scholar

[18] Xiangtai, L., Jiangning, Z., Yibo, Y., Guangliang, C., Kuiyuan, Y., & Yunhai, T., et al. (2024). Sfnet: faster and accurate semantic segmentation via semantic flow. International Journal of Computer Vision(2), 132. Search in Google Scholar

[19] Shekar, K. C., Cross, M. A., & Vasudevan, V. (2021). Optical Character Recognition and Neural Machine Translation Using Deep Learning Techniques. Search in Google Scholar

[20] Sanjanasri, J. P., Kumar, M. A., & Soman, K. P. (2020). Deep learning-based techniques to enhance the precision of phrase-based statistical machine translation system for indian languages. International Journal of Computer Aided Engineering and Technology, 13(1/2), 239. Search in Google Scholar

[21] Ghazala Nasreen,Muhammad Murad Khan,Muhammad Younus,Bushra Zafar & Muhammad Kashif Hanif.(2024).Email spam detection by deep learning models using novel feature selection technique and BERT.Egyptian Informatics Journal100473-. Search in Google Scholar

[22] Das Sanhita,Maurya Akhilesh Kumar & Dey Arka.(2024).A recurrent neural network model for predicting two-leader car-following behavior.Transportation Letters(5),461-475. Search in Google Scholar

[23] Ge He,Lei Luo,Li Zhou,Yiyang Dai,Xu Ji,Chao Guo & Zhaopeng Lu.(2024).Deep learning prediction of yields of fluid catalytic cracking via differential evolutionary dual-stage attention-based LSTM. Fuel131826-. Search in Google Scholar

[24] Xinyu Gu,Zheng Lu,Jianfeng Ren & Qian Zhang.(2024).Seat belt detection using gated Bi-LSTM with part-to-whole attention on diagonally sampled patches.Expert Systems With Applications(PA),123784-. Search in Google Scholar

[25] Yalin Tian,Zengzeng Lian,M. Amparo Núñez Andrés,Zhe Yue,Kezhao Li,Penghui Wang & Mengqi Wang.(2024).The application of gated recurrent unit algorithm with fused attention mechanism in UWB indoor localization.Measurement114835-. Search in Google Scholar