Year: 2024
Type: conference-paper
Abstract: Names carry important information about our identities and demographics such as gender, nationality, ethnicity, etc.We investigate the use of individual's name, in both Arabic and English, to predict important attributes, namely country, region, gender, and language.We extract data from Wikidata, and normalize it, to build a comprehensive dataset consisting of more than 1 million entities and their normalized attributes.We experiment with a Linear SVM approach, as well as two Transformers approaches consisting of BERT model fine-tuning and Transformers pipeline.Our results indicate that we can predict the gender, language and region using the name only with an accuracy of over 0.65.The country attribute can be predicted with less accuracy.The Linear SVM approach outperforms the other approaches for all the attributes.The best performing approach was also evaluated on another dataset that consists of 1,500 names from 15 countries (covering different regions) extracted from Twitter, and yields similar results.We share the datasets used in our experiments in addition to an online interface for testing and API calling.
Authors: Samir Abdaljalil, Hamdy Mubarak
Institution: Texas A&M University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402683920