1. Text matching: The duplicate checking system will split each sentence in the paper and compare it with the documents in the document library sentence by sentence. If sentences with high similarity are found, the system will think that there may be plagiarism in this part of the content.
2. Semantic analysis: The duplicate checking system will analyze the vocabulary, phrases and sentence structures in the paper to determine their semantic similarity. If a word or phrase with high similarity is found, the system will think that there may be plagiarism in this part of the content.
3. Citation detection: The duplicate checking system will check the citations and references in the paper to ensure that the author correctly quotes other people's research results. If there is any unmarked document content, the system will think that this part of the content may be plagiarized.
4. Format detection: The duplicate checking system will also check the format of the paper, such as font, font size and line spacing, to ensure the originality of the paper. If there is a format similar to other documents, the system will think that this part of the content may be plagiarized.
5. Method based on machine learning: In recent years, the duplicate checking system gradually adopts methods based on machine learning, such as support vector machine (SVM) and neural network, to improve the accuracy and efficiency of duplicate checking. These methods can automatically learn the feature representation of the text, so as to better identify plagiarism.
In short, the duplicate checking system comprehensively judges plagiarism in papers through various methods to ensure academic integrity and intellectual property protection. However, the duplicate checking system is not perfect, and sometimes there may be misjudgments or missed judgments. Therefore, the author should be cautious when using the duplicate checking system, and combine his own professional knowledge and judgment to ensure the originality of the paper.