Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
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Article Number | 01089 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202343001089 | |
Published online | 06 October 2023 |
Sustainable Multi-Author Writing Style Analysis for Identifying Stylistic Differences Between Authors
1 Department of Information technology, GRIET, Bachupally, Hyderabad, JNTUH, Telangana,India,500090.
2 School of Applied and Life Sciences, Uttaranchal University, Dehradun, 248007, India
* Corresponding author: nvgraju@griet.ac.in
Natural language processing (NLP) is a sustainable subfield of Artificial Intelligence that focuses on the interaction between computers and humans through natural language. NLP algorithms enable computers to comprehensively understand, interpret, and generate human language, thus facilitating the sustainable analysis and comprehension of vast amounts of textual data. Within the context of sustainable style change detection, NLP algorithms play a pivotal role in analyzing multi-author documents and identifying the points at which authors transition. This sustainable step is critical in authorship recognition as it furnishes a more precise comprehension of which sections were authored by different individuals. A multi-author document’s writing style can evolve over time, and this sustainability can prove invaluable in fields such as forensics, journalism, and literary studies, among others.The sustainable goal of this project is to investigate various NLP methods for sustainable style change detection. By scrutinizing datasets and juxtaposing them with advanced methodologies in the existing literature, the effectiveness of these strategies will be ascertained. The overarching aim of our study is to foster the progress of both the field of NLP research and its sustainable practical applications.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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