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We study the bias in several state-of-the-art named entity recognition (NER)models---specifically, a difference in the ability to recognize male and femalenames as PERSON entity types. We evaluate NER models on a dataset containing139 years of U.S. census baby names and find that relatively more female names,as opposed to male names, are not recognized as PERSON entities. We study theextent of this bias in several NER systems that are used prominently inindustry and academia. In addition, we also report a bias in the datasets onwhich these models were trained. The result of this analysis yields a newbenchmark for gender bias evaluation in named entity recognition systems. Thedata and code for the application of this benchmark will be publicly availablefor researchers to use.

Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition