Incorporating Natural Language Processing into Virtual Assistants
Year: 2026
Type: article
Abstract: - Virtual assistants have become an integral part of modern digital systems by enabling users to interact with applications using natural language. The effectiveness of such systems largely depends on their ability to correctly understand user intent and extract relevant information from textual input. This paper presents the design and implementation of a simple text-based virtual assistant using Natural Language Processing (NLP) techniques and machine learning algorithms. The proposed system focuses on intent classification and basic slot filling to interpret user commands such as making calls, retrieving weather information, and setting reminders. The implementation is carried out using Python and widely used open-source libraries including NLTK, spaCy, and scikit-learn. A lightweight machine learning model is trained on a small, publicly available dataset to ensure that the system can operate efficiently on low-resource environments. Experimental results demonstrate that the proposed approach achieves satisfactory accuracy for common user queries and is suitable for student-level applications. The system is easy to understand, implement, and extend, making it a practical foundation for future enhancements such as voice input, deep learning models, and multilingual support. The effectiveness of such systems largely depends on their ability to correctly understand user intent and extract relevant information from textual input. This paper presents the design and implementation of a simple text-based virtual assistant using Natural Language Processing (NLP) techniques and machine learning algorithms. The proposed system focuses on intent classification and basic slot filling to interpret user commands such as making calls, retrieving weather information, and setting reminders. The implementation is carried out using Python and widely used open-source libraries including NLTK, spaCy, and scikit-learn. A lightweight machine learning model is trained on a small, publicly available dataset to ensure that the system can operate efficiently on low-resource environments. Experimental results demonstrate that the proposed approach achieves satisfactory accuracy for common user queries and is suitable for student-level applications.
Authors: Raghav Sharma, Nandini Gupta, Vishrut Vyas
Institution: JECRC University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7125426558
FWCI:
Cites:
Cited by:
Related to: