Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names
This work addresses the problem of gender detection for Japanese names, which is incremental as it applies existing methods to a new dataset.
The paper tackles gender detection from Japanese names by introducing a novel dataset of 64,139 names in multiple forms and proposing the Gendec framework, which uses machine learning and transfer learning to predict gender accurately.
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.