The week's pick
Reseach Article
Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search
by
Shrishti Shiva,
Mohamed El-Dosuky,
Sherif Kamel
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 11 |
| Year of Publication: 2024 |
| Authors: Shrishti Shiva, Mohamed El-Dosuky, Sherif Kamel |
10.5120/ijca2024923468
|
Shrishti Shiva, Mohamed El-Dosuky, Sherif Kamel . Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search. International Journal of Computer Applications. 186, 11 ( Mar 2024), 39-45. DOI=10.5120/ijca2024923468
@article{
10.5120/ijca2024923468,
author = {
Shrishti Shiva,
Mohamed El-Dosuky,
Sherif Kamel
},
title = { Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search },
journal = {
International Journal of Computer Applications
},
issue_date = { Mar 2024 },
volume = { 186 },
number = { 11 },
month = { Mar },
year = { 2024 },
issn = { 0975-8887 },
pages = {
39-45
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume186/number11/natural-language-processing-and-natural-language-understanding-techniques-for-intelligent-search/
},
doi = { 10.5120/ijca2024923468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-03-23T00:18:05.472837+05:30
%A Shrishti Shiva
%A Mohamed El-Dosuky
%A Sherif Kamel
%T Natural Language Processing and Natural Language Understanding Techniques for Intelligent Search
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 11
%P 39-45
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents an intelligent text retrieval and ranking system leveraging advanced NLP and NLU techniques, including word embeddings and cosine similarity. The system incorporates an LSTM language model to generate document embeddings from preprocessed text documents, facilitating accurate document-query matching. Experimental evaluation demonstrates the system's efficacy, achieving an average accuracy of 0.75 on the test set. The use of cosine similarity further supports the system's ability to rank documents meaningfully. However, potential overfitting concerns necessitate an exploration of regularization techniques to improve generalization. The proposed intelligent system finds practical applications in search engines and recommendation systems, delivering contextually relevant content to users.
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Index Terms
Computer Science
Information Sciences
Natural language processing
Natural language understanding
search
Keywords