Document Type
Conference Proceeding
Source of Publication
Procedia Computer Science
Publication Date
4-25-2025
Abstract
Natural Language Processing (NLP) has transformed human-computer interaction, especially in the realm of virtual assistants. NLP enables machines to understand, interpret, and generate human language, driving innovations in applications ranging from virtual assistants to customer service chatbots. This paper delves into the intersection of NLP and virtual assistants, examining advanced models like BERT and RoBERTa, which enhance contextual understanding and user intent recognition. Through a comprehensive evaluation using the dataset of research abstracts to explore new methods and improve response for virtual assistant devices, it explores methods to improve model efficiency, precision, and scalability. By leveraging machine learning techniques and probabilistic models such as Naive Bayes and Hidden Markov Models, this research addresses key challenges in language comprehension and response accuracy. It also discusses the potential for smaller, more efficient models to optimize virtual assistant performance in real-time applications. The results highlight the ongoing advancements in NLP, aiming for a future where virtual assistants become more responsive, intuitive, and capable of supporting various industries with increased efficiency.
DOI Link
ISSN
Volume
257
First Page
1021
Last Page
1026
Disciplines
Computer Sciences
Keywords
BERT, Chatbot, ML, NLP, Probabilistic Models, RoBERTa, Virtual Assistants
Scopus ID
Recommended Citation
Alshahoomi, Reem; Alameri, Salma; Alfalasi, Sanaa; and Al-Obeidat, Feras, "The Role of Natural Language Processing in Abstract Dataset to Improve Virtual Assistant Devices" (2025). All Works. 7321.
https://zuscholars.zu.ac.ae/works/7321
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series