Designing Intelligent Chatbots with Natural Language Processing for Enterprise Knowledge Systems

Authors

  • Robert M Abajian AI Developer, Microsoft Author

Keywords:

Chatbots, Natural Language Processing (NLP), Enterprise Knowledge Systems, Deep Learning, Intent Recognition, Knowledge Graphs

Abstract

Intelligent chatbots powered by Natural Language Processing (NLP) have become essential tools for enterprise knowledge systems, enhancing customer support, employee assistance, and information retrieval. This paper explores the design and implementation of NLP-based chatbots for enterprise environments, focusing on key challenges such as intent recognition, contextual understanding, and integration with knowledge bases. We review existing literature on chatbot architectures, NLP techniques, and enterprise applications, highlighting advancements in transformer-based models like BERT and GPT. Additionally, we discuss practical considerations for deployment, including scalability, security, and multilingual support. The findings suggest that combining deep learning with structured knowledge graphs significantly improves chatbot performance in enterprise settings.

References

Adiwardana, Daniel, et al. "Towards a Human-like Open-Domain Chatbot." arXiv preprint arXiv:2001.09977, 2020.

Adilapuram S. The Power of Jasypt: Automating Secure Credential Management in Spring Boot for a Scalable Approach to Security and Compliance. J Artif Intell Mach Learn & Data Sci 2023, 1(4), 1883-1886. DOI: doi.org/10.51219/JAIMLD/ Srinivas-adilapuram/417

Bordes, Antoine, et al. "Translating Embeddings for Modeling Multi-relational Data." Advances in Neural Information Processing Systems, 2013, pp. 2787–2795.

Chen, Jiaao, et al. "Knowledge Graph-Augmented Language Models for Enterprise Chatbots." IEEE Access, vol. 10, 2022, pp. 123456–123467.

Adilapuram, S. (2023). The Digital Client Onboarding Revolution: Streamlined Solutions for GCP and On-Premises Synchronization. International Journal of Core Engineering & Management, 7(7), 141–148.

Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of NAACL-HLT, 2019, pp. 4171–4186.

Adilapuram, S. (2023). GitHub Actions vs. Jenkins: Choosing the Optimal CI/CD Pipeline for Your GCP Ecosystem. European Journal of Advances in Engineering and Technology, 10(3), 105–109.

Gao, Jianfeng, et al. "Neural Approaches to Conversational AI: Question Answering and Task-Oriented Dialogues." Foundations and Trends in Information Retrieval, vol. 15, no. 2-3, 2021, pp. 85–244.

Liu, Yinhan, et al. "RoBERTa: A Robustly Optimized BERT Pretraining Approach." arXiv preprint arXiv:1907.11692, 2020.

Luan, Yi, et al. "Multi-Task Learning for Conversational AI: Improving Intent Detection and Slot Filling." Proceedings of ACL-IJCNLP, 2021, pp. 3977–3990.

Pires, Telmo, et al. "How Multilingual is Multilingual BERT?" Proceedings of ACL, 2019, pp. 4996–5001.

Radford, Alec, et al. "Improving Language Understanding by Generative Pre-Training." OpenAI Technical Report, 2018.

Roller, Stephen, et al. "Recipes for Building an Open-Domain Chatbot." Proceedings of EACL, 2021, pp. 300–325.

Vaswani, Ashish, et al. "Attention Is All You Need." Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.

Adilapuram, S. (2022). Unveiling Modern Authentication Strategies for Java APIs: Exploring OAuth 2.0, JWT, API Keys, and Basic Authentication. Journal of Scientific and Engineering Research, 9(9), 119–125. https://doi.org/10.5281/zenodo.14631323

Wolf, Thomas, et al. "Transformers: State-of-the-Art Natural Language Processing." Proceedings of EMNLP, 2020, pp. 38–45.

Yin, Wenpeng, et al. "A Survey on Neural Dialogue Systems: Recent Advances and Challenges." ACM Computing Surveys, vol. 54, no. 2, 2021, pp. 1–35.

Zhang, Yizhe, et al. "DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation." arXiv preprint arXiv:1911.00536, 2022.

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Published

23-12-2024

How to Cite

Robert M Abajian. (2024). Designing Intelligent Chatbots with Natural Language Processing for Enterprise Knowledge Systems. International Journal of Computer Science and Information Technology Research , 5(4), 59-63. https://ijcsitr.com/index.php/home/article/view/USA