Can AI help me check for sentence repetitions in articles?

Artificial intelligence can effectively identify sentence-level repetitions within texts using natural language processing (NLP) algorithms. These systems analyze textual similarities by comparing semantic content and syntactic structures across sentences. This capability is fundamentally feasible and increasingly accurate due to advances in machine learning models trained on large linguistic datasets. Detection relies primarily on text vectorization techniques, such as TF-IDF or advanced embeddings like BERT, to represent sentence meaning numerically. Algorithms then compute similarity scores (e.g., cosine similarity) between these vectors. Crucially, effective tools differentiate near-duplicates, paraphrases, and accidental verbatim matches, though contextual nuance remains challenging. Users must calibrate similarity thresholds according to their specific needs for precision and recall, ensuring minimal false positives while capturing substantive redundancies. AI-powered repetition checking is frequently integrated into proofreading platforms like Grammarly Pro, Turnitin, or dedicated plagiarism detectors. Academics and authors employ it during manuscript revisions to enhance conciseness and originality. The workflow involves submitting text to the tool, reviewing flagged sentences and similarity percentages, and refining or citing sources as needed. This significantly improves editing efficiency, reduces inadvertent redundancy, and supports rigorous scholarly writing.