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A Natural Language Processing Framework for Document Similarity in Java Environments

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Bibliographic Details
Title: A Natural Language Processing Framework for Document Similarity in Java Environments
Authors: Ayan Hussain, Moh Zaid Khan, Rayyan Arif Hussain, Abdul Ahad, Ambreen Anees
Source: International Journal of Innovative Science and Research Technology. :4410-4415
Publisher Information: International Journal of Innovative Science and Research Technology, 2025.
Publication Year: 2025
Description: Document similarity plays a pivotal role in the field of Natural Language Processing (NLP), especially in tasks that require identifying the degree of relatedness between textual content. This paper presents a comprehensive study and implementation of document similarity techniques using the Java programming language, with a focus on practical NLP approaches. The motivation behind this work stems from real-world applications such as plagiarism detection, content recommendation systems, semantic search engines, and automated document classification. The system developed in this research employs a multi-step NLP pipeline beginning with data preprocessing. This includes default procedures such as text normalizing, tokenizing, stop word removal, and optional stemming or lemmatization. Following post-preprocessing, documents are converted into numerical vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting scheme, which determines how important terms are in each document in relation to the collection as a whole.Since cosine similarity is effective at comparing text-based vectors in a high-dimensional space, it is used to evaluate similarity among document vectors.
Document Type: Article
Language: English
DOI: 10.38124/ijisrt/25apr1961
Accession Number: edsair.doi...........5bb91d0faf6f181f882ea78fa2ed2296
Database: OpenAIRE
Description
Abstract:Document similarity plays a pivotal role in the field of Natural Language Processing (NLP), especially in tasks that require identifying the degree of relatedness between textual content. This paper presents a comprehensive study and implementation of document similarity techniques using the Java programming language, with a focus on practical NLP approaches. The motivation behind this work stems from real-world applications such as plagiarism detection, content recommendation systems, semantic search engines, and automated document classification. The system developed in this research employs a multi-step NLP pipeline beginning with data preprocessing. This includes default procedures such as text normalizing, tokenizing, stop word removal, and optional stemming or lemmatization. Following post-preprocessing, documents are converted into numerical vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting scheme, which determines how important terms are in each document in relation to the collection as a whole.Since cosine similarity is effective at comparing text-based vectors in a high-dimensional space, it is used to evaluate similarity among document vectors.
DOI:10.38124/ijisrt/25apr1961