Predictive Analysis and Email CategorizationUsing Large Language Models
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 7
Abstract
With the global rise in internet users, email communication has become an integral part of daily life. Categorizing emails based on their intent can significantly save time and boost productivity. While previous research has explored machine learning models, including neural networks, for intent classification, Large Language Models (LLMs) have yet to be applied to intent-based email categorization. In this study, a subset of 11,000 emails from the publicly available Enron dataset was used to train various LLMs, including Bidirectional Encoder Representations from Transformers (BERT), Distil BERT, XLNet, and Generative Pre-training Transformer (GPT-2) for intent classification. Among these models, Distil BERT achieved the highest accuracy at 82%, followed closely by BERT with 81%. This research demonstrates the potential of LLMs to accurately identify the intent of emails, providing a valuable tool for email classification and management.
Authors and Affiliations
Sabih Ahmed, Junaid Mazhar Muhammad, Muhammad Farrukh Shahid, M. Hassan Tanveer, Rabab Raza
Keywords
Related Articles
- EP ID EP761768
- DOI -
- Views 13
- Downloads 0
Leveraging CIELAB Segmentation and CNN for Wheat Fungi Disease Classification
Wheat is the third most harvested and consumed grain globally, but a significant portion of its production is wasted due to diseases. Fungal infections caused by pathogenic fungi are particularly harmful, greatly reduc...
Vortex Powerplant Implementation in A Coastal Community
A gravitational water vortex power plant is an eco-friendly device that generates electricity from renewable energy sources. In this system, a turbine extracts energy from the vortex created by tangentially channeling...
An Artificial Intelligence VisionTransformer Model for Classification of Bacterial Colony
The application of AI and machine learning, particularly the vision transformer method, in bacterial detection presents a promising solution to overcome limitations of traditional methods, offering faster and more accu...
Using Spatial Covariance of Geometric and Shape Based Features for Recognition of Basic and Compound Emotions
Introduction / Importance of Study: Compound emotion recognition has been an emerging area of research for the last decade due to its vast applications in surveillance systems, suspicious person detection, detection of...
AI-Powered Detection: Implementing Deep Learning for Breast Cancer Prediction
Breast cancer remains a critical global health issue, affecting millions of women worldwide. According to the World Health Organization (WHO), there were 2.3 million new cases and 685,000 deaths from breast cancer in 2...