DEVELOPMENT AND IMPLEMENTATION OF ADVANCED NATURAL LANGUAGE PROCESSING MODELS FOR EFFICIENT BUSINESS COMMUNICATION SOLUTIONS
Keywords:
Natural Language Processing, Business Communication, Sentiment Analysis, Chatbots, Automated Reporting, Predictive Text ModelsAbstract
The growing need for efficient business communication has propelled the adoption of advanced Natural Language Processing (NLP) models. These models enable businesses to process, analyze, and generate human-like language, thus optimizing workflows, enhancing customer experiences, and streamlining operations. This paper explores the development and implementation of NLP models tailored for business applications, delving into their evolution, practical applications, and future potential. Key areas of discussion include sentiment analysis, chatbot systems, automated reporting, and predictive text models.
References
Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of NAACL-HLT, 2018. Link.
Brown, Tom, et al. "Language Models Are Few-Shot Learners." Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1877–1901. Link.
Vaswani, Ashish, et al. "Attention is All You Need." Advances in Neural Information Processing Systems, vol. 30, 2017. Link.
Kumar, Anuj, and Rajesh Gupta. "Applications of NLP in Business Communication." International Journal of Business Communication, vol. 56, no. 3, 2021, pp. 210–227
Liu, Yinhan, et al. "RoBERTa: A Robustly Optimized BERT Pretraining Approach." arXiv preprint arXiv:1907.11692., 2019. Link.
Radford, Alec, et al. "Language Models Are Unsupervised Multitask Learners." OpenAI Blog, 2019. Link.
Jurafsky, Daniel, and James H. Martin. Speech and Language Processing. 3rd ed., draft, 2021. Link.
Young, Tom, et al. "Recent Trends in Deep Learning Based Natural Language Processing." IEEE Computational Intelligence Magazine, vol. 13, no. 3, 2018, pp. 55–75.
Zhang, Yu, and Qiang Yang. "A Survey on Multi-Task Learning." IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, 2021, pp. 5586–5609.
Howard, Jeremy, and Sebastian Gugger. "Universal Language Model Fine-Tuning for Text Classification." Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018.
Clark, Kevin, et al. "What Does BERT Look At? An Analysis of BERT’s Attention." Proceedings of the 2019 ACL Workshop BlackboxNLP, 2019. Link.
Ruder, Sebastian, et al. "Transfer Learning in Natural Language Processing." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 2019.
Cambria, Erik, et al. "New Avenues in Knowledge Bases for Natural Language Processing." Journal of Artificial Intelligence Research, vol. 69, 2020, pp. 229–255.