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.

该研究旨在改进个人姓名的识别，在识别不同文化背景下的姓名方面，通过实验比较结合字符和词的输入模型与传统的NER模型，发现该模型表现更好且可能提高准确率。

跨文化姓名识别