Comparative Analysis of BERT and TF-IDF for Textual Semantic Similarity Assessment

The paper presents an investigative analysis between two techniques BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency-Inverse Document Frequency) combined with cosine similarity. Text comparison, in general, is a fundamental aspect of NLP, plays a crucial role in a variety of applications, such as search engines, plagiarism detection, and question-answering systems. The objective is to evaluate efficiency of techniques in comparing similarity between text and to identify which one performs better. The experiments demonstrate that while BERT excels in understanding context-dependent meanings and subtle linguistic nuances, TF-IDF with cosine similarity offers computational efficiency and interpretability for specific task. Findings provide insights into the strengths and limitations of each approach, offering guidance for researchers and practitioners in selecting appropriate methodologies for content relevance assessment in diverse scenarios.