Gendec: A Machine Learning-based Framework for Gender Detection from
  Japanese Names
Duong Tien Pham Luan Thanh Nguyen
Every human has their own name, a fundamental aspect of their identity and
cultural heritage. The name often conveys a wealth of information, including
details about an individual's background, ethnicity, and, especially, their
gender. By detecting gender through the analysis of names, researchers can
unlock valuable insights into linguistic patterns and cultural norms, which can
be applied to practical applications. Hence, this work presents a novel dataset
for Japanese name gender detection comprising 64,139 full names in romaji,
hiragana, and kanji forms, along with their biological genders. Moreover, we
propose Gendec, a framework for gender detection from Japanese names that
leverages diverse approaches, including traditional machine learning techniques
or cutting-edge transfer learning models, to predict the gender associated with
Japanese names accurately. Through a thorough investigation, the proposed
framework is expected to be effective and serve potential applications in
various domains.