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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | An Approach for Writing Style Change Detection using Pre-trained BERT model with similarity measures |
| Authors Name: | Dr. T. Raghunadha Reddy , Naveed Wasim , Mohd Muzzammil Hassan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_194707
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| Published Paper Id: | IJNRD2305382 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Detecting changes in writing style is an important task in authorship profiling, with one of its primary applications being plagiarism detection. The task aims to identify any areas in a document where there are stylistic changes, which can help estimate the number of authors of the document. This paper proposes a method for Style Change Detection that uses a pre-trained BERT model for detecting writing style changes in a given text corpus. BERT (Bidirectional Encoder Representations from Transformers) is an open-source bidirectional model by Google AI that can tokenize and generate embeddings for text data. To implement this approach, the BERT model will be fine-tuned on a dataset of known writing style changes, and then used to measure the similarity between adjacent segments of text in a given document. The model will compare the similarity scores of adjacent segments to identify areas where there is a change in writing style. This paper aims to explore the effectiveness of using pre-trained language models for writing style change detection and provide insights into how such models can be used in various text processing tasks. Overall, the proposed method has several potential benefits, including improved accuracy in identifying writing style changes and scalability to larger datasets. This could have significant implications for the field of authorship profiling and plagiarism detection, as it could potentially improve the efficiency and accuracy of these processes. Moreover, this approach can provide a foundation for future research in using pre-trained language models for text processing tasks beyond writing style change detection. |
| Keywords: | Style Change Detection, BERT, Similarity Measures. |
| Cite Article: | "An Approach for Writing Style Change Detection using Pre-trained BERT model with similarity measures", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d653-d658, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305382.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305382 Registration ID: 194707 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: d653-d658 Country: Hyderabad, Telangana, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305382 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305382 |
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ISSN: 2456-4184
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