This work is licensed under the Creative Commons Attribution 4.0 International License.
Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649.Search in Google Scholar
Eisenstein, J. (2019). Introduction to natural language processing. MIT press.Search in Google Scholar
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713-3744.Search in Google Scholar
Thanaki, J. (2017). Python natural language processing. Packt Publishing Ltd.Search in Google Scholar
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Rush, A. M. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations (pp. 38-45).Search in Google Scholar
Goldberg, Y. (2017). Neural network methods in natural language processing. Morgan & Claypool Publishers.Search in Google Scholar
McShane, M. (2017). Natural language understanding (NLU, not NLP) in cognitive systems. AI Magazine, 38(4), 43-56.Search in Google Scholar
Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., & Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55(8), 1-38.Search in Google Scholar
Maulud, D. H., Zeebaree, S. R., Jacksi, K., Sadeeq, M. A. M., & Sharif, K. H. (2021). State of art for semantic analysis of natural language processing. Qubahan academic journal, 1(2), 21-28.Search in Google Scholar
Salloum, S. A., Khan, R., & Shaalan, K. (2020). A survey of semantic analysis approaches. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (pp. 61-70). Springer International Publishing.Search in Google Scholar
Harispe, S., Ranwez, S., & Montmain, J. (2022). Semantic similarity from natural language and ontology analysis. Springer Nature.Search in Google Scholar
Gong, Y., Lu, N., & Zhang, J. (2019). Application of deep learning fusion algorithm in natural language processing in emotional semantic analysis. Concurrency and Computation: Practice and Experience, 31(10), e4779.Search in Google Scholar
Sharonova, N., Kyrychenko, I., Gruzdo, I., & Tereshchenko, G. (2022, May). Generalized Semantic Analysis Algorithm of Natural Language Texts for Various Functional Style Types. In COLINS (pp. 16-26).Search in Google Scholar
Bharambe, U., Narvekar, C., & Andugula, P. (2022). Ontology and knowledge graphs for semantic analysis in natural language processing. In Graph Learning and Network Science for Natural Language Processing (pp. 105-130). CRC Press.Search in Google Scholar
Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing.Search in Google Scholar
Goyal, P., Pandey, S., & Jain, K. (2018). Deep learning for natural language processing. New York: Apress.Search in Google Scholar
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3), 55-75.Search in Google Scholar
Gimenez, M., Palanca, J., & Botti, V. (2020). Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing, 378, 315-323.Search in Google Scholar
Zhang, B., Yan, H., Wu, J., & Qu, P. (2024). Application of Semantic Analysis Technology in Natural Language Processing. Journal of Computer Technology and Applied Mathematics, 1(2), 27-34.Search in Google Scholar
Jaroli, P., Singla, C., Bhardwaj, V., & Mohapatra, S. K. (2022, April). Deep learning model based novel semantic analysis. In 2022 2nd international conference on advance computing and innovative technologies in engineering (ICACITE) (pp. 1454-1458). IEEE.Search in Google Scholar
Li Xiang. (2024). Legal text basic element identification based on the BERT model in the judicial field. Journal of Computational Methods in Sciences and Engineering(4-5),2333-2342.Search in Google Scholar
Huirong Chen,Song Liu,Ximing Yang,Xinggang Zhang,Jianzhong Yang & Shaofen Fan. (2024). Prediction of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism. Electronics(14),2804-2804.Search in Google Scholar
Gu Jianan,Ren Kehao & Gao Binwei. (2024). Deep learning-based text knowledge classification for whole-process engineering consulting standards. Journal of Engineering Research(2),61-71.Search in Google Scholar
Zhao Ruiye. (2024). Volleyball training video classification description using the BiLSTM fusion attention mechanism. Heliyon(15),e34735-e34735.Search in Google Scholar
Liu Hengwei & Gu Xiaodong. (2024). Masked co-attention model for audio-visual event localization. Applied Intelligence(2),1691-1705.Search in Google Scholar
Fan Cao,Bo Liu,Kai Wang,Yanshan Xiao,Jinghui He & Jian Xu. (2024). Dictionary-based multi-instance learning method with universum information. Information Sciences121264-121264.Search in Google Scholar