Semantic Similarity Detection and Analysis For Text Documents

This paper delves into the critical task of measuring semantic similarity in text documents, a fundamental need in today's data-rich landscape. Efficiently gauging semantic con-nections is vital for applications such as information retrieval, content recommendation, and document clustering. Traditional methods, centered on lexical and syntactic features, often fall short, especially when dealing with synonyms, abbreviations, or diverse language use. Consequently, there's a growing demand for advanced techniques that can better capture the underlying semantic relationships in text documents. The study introduces a comprehensive approach to semantic similarity analysis for text documents. This approach involves the development and evaluation of innovative methodologies that utilize advanced natural language processing and machine learning techniques. It explores the integration of domain-specific knowledge bases, advanced feature representations, and semantic embeddings to enhance the accuracy of similarity detection. Additionally, adaptable synonym repositories are investigated to align with evolving language trends. These efforts collectively aim to advance the field of semantic similarity detection, ultimately improving the precision and efficiency of document analysis across a broad spectrum of text-based applications.