文章基本信息

标题：How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors
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作者：Esam Alzahrani ; Leon Jololian
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：15
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Forensic author profiling plays an important role in indicating possible profiles for suspects. Among the many automated solutions recently proposed for author profiling, transfer learning outperforms many other state-of-the-art techniques in natural language processing. Nevertheless, the sophisticated technique has yet to be fully exploited for author profiling. At the same time, whereas current methods of author profiling, all largely based on features engineering, have spawned significant variation in each model used, transfer learning usually requires a preprocessed text to be fed into the model. We reviewed multiple references in the literature and determined the most common preprocessing techniques associated with authors' genders profiling. Considering the variations in potential preprocessing techniques, we conducted an experimental study that involved applying five such techniques to measure each technique’s effect while using the BERT model, chosen for being one of the most-used stock pretrained models. We used the Hugging face transformer library to implement the code for each preprocessing case. In our five experiments, we found that BERT achieves the best accuracy in predicting the gender of the author when no preprocessing technique is applied. Our best case achieved 86.67% accuracy in predicting the gender of authors.
关键词：Authorship profiling;NLP;digital forensics;transfer learning