Integrating Natural Language Processing (NLP) for Real-Time Communication and Collaboration in Project Management
Published 05-12-2023
Keywords
- natural language processing,
- project management
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Natural Language Processing (NLP) has emerged as a pivotal technology in enhancing real-time communication and collaboration in project management. This paper explores the role of NLP in automating document analysis, meeting minutes, and stakeholder communications, thereby improving efficiency and productivity within project teams. By analyzing how NLP can streamline communication processes, the paper identifies key applications such as automated transcription, sentiment analysis, and intelligent document processing. Furthermore, it discusses the integration of NLP tools into existing project management frameworks, emphasizing the benefits and challenges associated with their adoption. The findings suggest that the strategic use of NLP can lead to significant improvements in project outcomes by facilitating clearer communication and collaboration among team members.
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References
- Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
- Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
- Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
- Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
- Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
- Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
- Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
- Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
- Katari, Pranadeep, et al. "Remote Project Management: Best Practices for Distributed Teams in the Post-Pandemic Era." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 145-167.
- Y. Bengio, "Learning deep architectures for AI," Foundations and Trends® in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009.
- H. He, Y. Bai, E. Kanoulas, and C. S. Jensen, "Learning to rank from natural language questions," in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 2532-2541.