How to do Named Entity Recognition (NER) with Python?

Named Entity Recognition (NER) is a crucial process in natural language processing that involves identifying and categorizing entities in text, such as names, organizations, and locations. Originating from the Message Understanding Conferences in the 1990s, NER has evolved from manual rules and dictionaries to advanced supervised learning techniques. When selecting an NER engine, choices include open source options like spaCy and NLTK, which allow for code modification and data privacy but require server maintenance, and cloud-based solutions like Google Cloud and IBM Watson, which offer high performance and ease of use but involve data being processed externally. A third option is building a custom NER engine, ensuring tailored performance and data security if the necessary expertise is available. Eden AI simplifies the process by providing a unified API that enables users to switch between multiple providers, ensuring optimal performance and compliance with data protection regulations.

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