BERT for NER

BERT models, when fine-tuned on Named Entity Recognition (NER), can have a very competitive performance for the English language. This is an overview of how BERT is designed and how it can be applied to the task of NER. In the last section, I will discuss a cross-lingual scenario. In this post, I will assume a basic familiarity with the NER task. When I talk about implementation details of BERT ​(Devlin et al., 2019)​, I am referring to the PyTorch version that was open-sourced by Hugging Face. I have not checked if it completely matches the original implementation with respect […]

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How NLP affects Gender Equality

More and more applications of Natural Language Processing (NLP) are used in everyday life: When you translate a paragraph online, you are using an application of NLP, and also when you dictate a letter to a speech recognition system or when you ask questions to a voice assistant. The business world, too, is full of hidden but powerful applications of NLP. Given this increasing applicability, researchers need to be aware of ethical concepts such as gender equality. First of all, NLP makes use of gender as an explicit variable in many places. For example, state-of-the-art systems can infer the gender […]

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