Result: A Natural Language Processing Framework for Document Similarity in Java Environments

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.