UNDERSTANDING NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES
Keywords:
Natural Language Processing (NLP), Sentiment Analysis, Language Generation, Machine Learning, Artificial IntelligenceAbstract
Artificial intelligence, linguistics, and cognitive psychology all come together in Natural Language Processing (NLP), an area that is changing quickly. This article discusses NLP's main methods, practical uses, and possible future developments. Key NLP techniques like sentiment analysis, language generation, and named object recognition are studied in depth and have various uses. NLP's Significant effects are discussed in many areas, such as virtual helpers, translation services, healthcare, finance, and education. NLP has come quite a way, but it still has issues with unclear words, understanding more than one language, and moral issues. More improvements will be made to model speed, interpretability, multimodal integration, and using common sense in the future. This review emphasizes NLP's transformative potential to change how people deal with computers and how information is processed in the digital age.
References
D. Jurafsky and J. H. Martin, "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition," 3rd ed. (draft), 2023. [Online]. Available: https://web.stanford.edu/~jurafsky/slp3/
Y. Goldberg, "Neural Network Methods for Natural Language Processing," Synthesis Lectures on Human Language Technologies, vol. 10, no. 1, pp. 1-309, 2017. [Online]. Available: https://doi.org/10.2200/S00762ED1V01Y201703HLT037
B. Liu, "Sentiment Analysis: Mining Opinions, Sentiments, and Emotions," 2nd ed. Cambridge University Press, 2020. [Online]. Available: https://doi.org/10.1017/9781108639286
A. Vaswani et al., "Attention Is All You Need," in Advances in Neural Information Processing Systems, 2017, pp. 5998-6008. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
J. Li, A. Sun, J. Han, and C. Li, "A Survey on Deep Learning for Named Entity Recognition," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 50-70, 2022. [Online]. Available: https://doi.org/10.1109/TKDE.2020.2981314
T. B. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems, 2020, pp. 1877-1901. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
E. Cambria and B. White, "Jumping NLP Curves: A Review of Natural Language Processing Research," IEEE Computational Intelligence Magazine, vol. 9, no. 2, pp. 48-57, 2014. [Online]. Available: https://doi.org/10.1109/MCI.2014.2307227
K. Kreimeyer et al., "Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review," Journal of Biomedical Informatics, vol. 73, pp. 14-29, 2017. [Online]. Available: https://doi.org/10.1016/j.jbi.2017.07.012
T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent Trends in Deep Learning Based Natural Language Processing," IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55-75, 2018. [Online]. Available: https://doi.org/10.1109/MCI.2018.2840738
J. Qiu, H. Chen, Y. Xie, X. Luo, and M. Huang, "A Survey on Deep Learning for Named Entity Recognition," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 50-70, 2022. [Online]. Available: https://doi.org/10.1109/TKDE.2020.2981314
