  State of the art Named Entity Recognition (NER) models have achieved an
impressive ability to extract common phrases from text that belong to labels
such as location, organization, time, and person. However, typical NER systems
that rely on having seen a specific entity in their training data in order to
label an entity perform poorly on rare or unseen entities ta in order to label
an entity perform poorly on rare or unseen entities (Derczynski et al., 2017).
This paper attempts to improve recognition of person names, a diverse category
that can grow any time someone is born or changes their name. In order for
downstream tasks to not exhibit bias based on cultural background, a model
should perform well on names from a variety of backgrounds. In this paper I
experiment with the training data and input structure of an English Bi-LSTM
name recognition model. I look at names from 103 countries to compare how well
the model performs on names from different cultures, specifically in the
context of a downstream task where extracted names will be matched to
information on file. I find that a model with combined character and word input
outperforms word-only models and may improve on accuracy compared to classical
NER models that are not geared toward identifying unseen entity values.
