Multicultural Name Recognition For Previously Unseen Names
This addresses bias in downstream tasks for multicultural name recognition, but it is incremental as it builds on existing Bi-LSTM models with minor modifications.
The paper tackles the problem of Named Entity Recognition (NER) models performing poorly on rare or unseen person names, especially from diverse cultural backgrounds, by experimenting with training data and input structures. The result shows that a model with combined character and word input outperforms word-only models and may improve accuracy compared to classical NER models.
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.