About this Research Topic
Natural Language Processing (NLP) is a subfield and a key technology of artificial intelligence. In recent years, many highly recognized efforts in NLP have emerged. NLP is a field where the use of Machine/Deep learning-based models in the past few years has allowed Artificial Intelligence (AI) to advance toward human levels like performances in different real-world applications. The significant advances in NLP have brought significant opportunities but also opened up new challenges for research. In this Research Topic collection, researchers and practitioners, both from academia and industry are invited to contribute with research work that presents significant originality, concepts, and methods in the field of NLP and AI applied to various areas of plagiarism detection. Furthermore, these applications have reached most languages and especially those with low and limited resources. This Research Topic will aim at gathering state-of-the-art research and development in not only NLP applications but will include new theoretical frameworks and methodologies.
This Research Topic addresses plagiarism detection. Due to the growing amount of information available on the internet, it makes it easier for someone to pass off and claim someone else's ideas as their own work without properly crediting the original source or owner. As a matter of ethics, plagiarism should be avoided. Currently, both academic and non-academic communities have become concerned about this issue. When someone plagiarises, they make an effort to pass off another person's contribution or words as their own. Furthermore, other forms of plagiarism involve taking credit for results, inventions, and mental activities produced by other people without acknowledgment. Additionally, it is also considered plagiarism to present someone else’s knowledge or idea as one’s own.
Topics of interest include but are not limited to the following:
• Architectures and systems for plagiarism detection
• External/Intrinsic plagiarism detection
• Cross-lingual plagiarism detection for low-resource languages
• Self-plagiarism or multiple submissions to different journals
• NLP-based deep learning approaches for plagiarism detection
• Short text similarity measurement
This Research Topic addresses plagiarism detection. Due to the growing amount of information available on the internet, it makes it easier for someone to pass off and claim someone else's ideas as their own work without properly crediting the original source or owner. As a matter of ethics, plagiarism should be avoided. Currently, both academic and non-academic communities have become concerned about this issue. When someone plagiarises, they make an effort to pass off another person's contribution or words as their own. Furthermore, other forms of plagiarism involve taking credit for results, inventions, and mental activities produced by other people without acknowledgment. Additionally, it is also considered plagiarism to present someone else’s knowledge or idea as one’s own.
Topics of interest include but are not limited to the following:
• Architectures and systems for plagiarism detection
• External/Intrinsic plagiarism detection
• Cross-lingual plagiarism detection for low-resource languages
• Self-plagiarism or multiple submissions to different journals
• NLP-based deep learning approaches for plagiarism detection
• Short text similarity measurement
Keywords: Natural language processing, Artificial intelligence, Automatic plagiarism detection, String matching, Semantic analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
