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Optimal Distributed Generator Sizing and Placement by Modified Particle Swarm Optimization (MPOS) Algorithm for Minimization of Distribution Power Loss

Anshu Sharma Rajesh Kumar Rai Ghizal Ansari
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Natural Language Processing in Conversational Systems: An Overview

by Oke Oluwafemi Ayotunde, Nadire Cavus
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Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 4
Year of Publication: 2025
Authors: Oke Oluwafemi Ayotunde, Nadire Cavus
10.5120/jaai202418

Oke Oluwafemi Ayotunde, Nadire Cavus . Natural Language Processing in Conversational Systems: An Overview. Journal of Advanced Artificial Intelligence. 1, 4 ( Jan 2025), 15-20. DOI=10.5120/jaai202418

@article{ 10.5120/jaai202418,
author = { Oke Oluwafemi Ayotunde, Nadire Cavus },
title = { Natural Language Processing in Conversational Systems: An Overview },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Jan 2025 },
volume = { 1 },
number = { 4 },
month = { Jan },
year = { 2025 },
pages = { 15-20 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume1/number4/natural-language-processing-in-conversational-systems-an-overview/ },
doi = { 10.5120/jaai202418 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-01T00:09:13.418371+05:30
%A Oke Oluwafemi Ayotunde
%A Nadire Cavus
%T Natural Language Processing in Conversational Systems: An Overview
%J Journal of Advanced Artificial Intelligence
%V 1
%N 4
%P 15-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Natural language processing, or NLP for short, has emerged as an essential component of conversational systems because it makes it possible for machines to comprehend and generate human language. NLP has been crucial to the development of conversational systems like chatbots and voice assistants, which have gained widespread popularity in recent years. NLP techniques like named entity recognition, sentiment analysis, and machine translation have been used to improve conversational systems' ability to comprehend user queries and respond appropriately. Strong NLP models, such as transformer-based structures, have also been developed as a result of advances in profound learning. These models have significantly improved conversational frameworks' capacity to generate normal and clear responses. The history, current state of the art, and prospects of NLP in conversational systems are all discussed in this paper. We likewise examine the difficulties related with NLP in conversational frameworks, for example, language understanding and logical mindfulness, and investigate expected answers for address these difficulties. In the end, this paper focuses on how important NLP is to the development of conversational systems and what it can do. In conversational systems, NLP technology's capacity to comprehend the context and purpose of the user's questions enables it to provide more precise and pertinent responses, which is advantageous to shareholders.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence natural language processing conversational systems chatbots language translation virtual assistant.

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