Abstract :

Natural Language Processing (NLP)-based Content Search Systems Aim To Enhance The Way Users Retrieve Information By Understanding The Semantic Meaning Of Queries Rather Than Relying Solely On Keyword Matching. Traditional Search Engines Often Fail To Capture The Context And Intent Behind User Queries, Leading To Irrelevant Or Incomplete Results. The Integration Of NLP Enables Machines To Comprehend Human Language More Effectively, Allowing Users To Search Using Natural, Conversational Phrases. The Proposed NLP-based Content Search System Leverages Techniques Such As Tokenization, Part-ofspeech Tagging, Named Entity Recognition (NER), Word Embeddings, And Semantic Similarity Analysis To Interpret Queries And Match Them With The Most Contextually Relevant Content. Advanced Models Like BERT (Bidirectional Encoder Representations From Transformers) And Word2Vec Are Utilized To Capture Deep Contextual Relationships Between Words And Documents. This Approach Significantly Improves Search Accuracy, Relevance, And User Satisfaction By Returning Results That Align With The User’s Intent Rather Than Exact Keyword Matches. NLP-based Content Search Finds Wide Applications In Search Engines, Academic Research Tools, Customer Support Systems, And Digital Libraries, Making Information Retrieval More Intelligent, Human-like, And Efficient. Keywords: Natural Language Processing, Content Search, Semantic Analysis, Machine Learning, BERT, Word Embeddings, Information Retrieval

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

186-191

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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