Research on English Reading Comprehension Strategies Based on Natural Language Processing

[1] Cui, M. (2022). Driis: research on automatic recognition of artistic conception of classical poems based on deep learning. International Journal of Cooperative Information Systems. Search in Google Scholar

[2] Ni, P., Li, Y., Li, G., & Chang, V. (2021). A hybrid siamese neural network for natural language inference in cyber-physical systems. ACM Transactions on Internet Technology, 21(2), 1-25. Search in Google Scholar

[3] Wang, Y., Zhang, H., Wang, S., Long, Y., & Yang, L. (2020). Semantic combined network for zero-shot scene parsing. IET Image Processing, 14(4). Search in Google Scholar

[4] Zhang, T., Jiang, H., Luo, X., & Chan, A. T. S. (2018). A literature review of research in bug resolution: tasks, challenges and future directions. Computer Journal, 59(5), 741-773. Search in Google Scholar

[5] A, S. Q., A, Q. L., A, S. Z., & C, W. H. B. (2022). Adversarial attack and defense technologies in natural language processing: a survey. Neurocomputing, 492, 278-307. Search in Google Scholar

[6] Kim, C. G., Hwang, Y. J., & Kamyod, C. (2022). A study of profanity effect in sentiment analysis on natural language processing using ann. J. Web Eng., 21. Search in Google Scholar

[7] Kremmel, B., Brunfaut, T., & Alderson, J. C. (2017). Exploring the role of phraseological knowledge in foreign language reading. Applied Linguistics, 38(6), 848-870. Search in Google Scholar

[8] Sadeghi, K., Khezrlou, S., & Modirkhameneh, S. (2017). Calling iranian learners of l2 english: effect of gloss type on lexical retention and reading performance under different learning conditions. Journal of Research in Reading. Search in Google Scholar

[9] Gerlach, & David. (2017). Reading and spelling difficulties in the elt classroom. ELT Journal. Search in Google Scholar

[10] Tsujii, J. (2021). Natural language processing and computational linguistics. Computational Linguistics, 47, 707-727. Search in Google Scholar

[11] Yue, T. (2021). English spoken stress recognition based on natural language processing and endpoint detection algorithm. International Journal of Electrical Engineering Education, 002072092098353. Search in Google Scholar

[12] Jang, H., Jeong, Y., & Yoon, B. (2021). Techword: development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications, 164, 114042. Search in Google Scholar

[13] He, H. (2018). The parallel corpus for information extraction based on natural language processing and machine translation. Expert Systems, 36(4), e12349. Search in Google Scholar

[14] Zheng, Z. (2021). Logical intelligent detection algorithm of chinese language articles based on text mining. Mobile information systems. Search in Google Scholar

[15] Khezrlou, S., Ellis, R., & Sadeghi, K. (2017). Effects of computer-assisted glosses on efl learners’ vocabulary acquisition and reading comprehension in three learning conditions. System, 65, 104-116. Search in Google Scholar

[16] Hasan, K., & Shabdin, A. A. (2017). Engineering efl learners" vocabulary depth knowledge and its relationship and prediction to academic reading comprehension. Asia Pacific Journal of Education, 2, 14-21. Search in Google Scholar

[17] Phung, C. K., & Phuong, H. Y. (2020). The impacts of implementing the flipped model on efl high school students’ reading comprehension. European Journal of Education, 7(11), 413-429. Search in Google Scholar

[18] Shang, H. F. (2017). Exploring metacognitive strategies and hypermedia annotations on foreign language reading. Interactive Learning Environments, 25(5-8), 610-623. Search in Google Scholar