Humanities and Arts
Automatic Detection of Plagiarism in Writing
Abstract
Key Questions about Plagiarism Detection in Writing
What methods were used to detect plagiarism in the study?
The study utilized tf-idf (term frequency-inverse document frequency) scores to represent text documents and cosine similarity measures to assess the similarity between source and suspicious documents. This approach enabled the identification of text overlap, indicating potential plagiarism.
What were the outcomes of the plagiarism detection model?
The model successfully created vectors and measured similarity metrics, producing a matrix of cosine similarity for all documents. This matrix facilitated manual retrieval of documents to check for overlap using online tools. While the algorithm was not extended to automatically retrieve related documents, the manual comparisons provided some validation of the model's effectiveness.
What are the limitations of the current plagiarism detection model?
The primary limitation is that the algorithm was not extended to automatically retrieve related documents for detailed analysis. Implementing a pipeline that converts texts to n-grams for in-depth analysis and evaluating the accuracy of the model could enhance its effectiveness in detecting plagiarism.
How can the plagiarism detection model be improved?
To improve the model, extending the algorithm to automatically retrieve related documents and converting texts to n-grams for detailed analysis would be beneficial. This approach would allow for a more accurate evaluation of the model and facilitate the identification of the best match as a source of plagiarism.
What is the significance of this study in the field of plagiarism detection?
This study contributes to the field by providing preliminary steps towards creating an external plagiarism detection tool. It highlights the potential of using tf-idf scores and cosine similarity measures in identifying text overlap, offering a foundation for developing more advanced plagiarism detection systems.
By addressing these questions, the study offers insights into the development and evaluation of plagiarism detection tools, emphasizing the importance of accurate and efficient methods in maintaining academic integrity.
