Software comparison of scientific article annotations using statistical natural language processing methods

Abstract

This paper presents an advanced approach to the automated comparison of scientific article abstracts using statistical natural language processing (NLP) techniques. The authors analyze state-of-the-art methodologies, including Cosine Similarity, Jaccard Similarity, and TF-IDF, alongside clustering methods and machine learning models such as SciBERT and BioBERT. A research-based software model is proposed to enhance text similarity assessment, facilitating efficient scientific literature analysis, reducing research duplication, and improving bibliographic accuracy. The study highlights the practical application of NLP techniques in academic publishing, plagiarism detection, and automated literature review systems. The proposed system integrates various computational approaches to refine text analysis and classification, making it a valuable tool for researchers and journal editors. Future research directions include optimizing NLP algorithms, incorporating deep learning methods, and integrating the system with major scientific databases to enhance further its applicability and performance in academic and industrial contexts.