Context-sensitive gender inference of named entities in text

Sudeshna Das, Jiaul H. Paik

Abstract: Highlights • We create four open-source datasets for identifying the gender of named entities. • Propose a novel transformer-based architecture for gender tagging of named entities. • Present multiple supervised and unsupervised learning baselines for gender inference. • Context-sensitive supervised learning outperforms database-reliant gender tagging. Abstract The gender information of named entities is an important prerequisite for many text analysis tasks such as gender bias detection and targeted advertising. Despite its valuable use cases, gender tagging of named entities has traditionally been database-reliant. The lack of open-source benchmarks is a major impediment to exploring the effectiveness of machine learning-based methods for this task. Towards this goal, the article serves two main purposes. Firstly, we create four open-source datasets from well-known NER corpora and make them publicly available. Secondly, we propose a novel supervised learning approach based on the transformer network to identify the gender of named entities. We evaluate the proposed approach on four gender identification datasets. The proposed method outperforms two commercial database-reliant approaches and five deep sequence models, including BERT.