Enhancing Natural Language Processing with Rule-based Code

The chapter explores the benefits of breaking down NLP problems into smaller components using deterministic rules-based code for predictability and troubleshooting. It delves into language-specific challenges and NLP nuances, emphasizing the importance of individualized approaches like tokenization for Chinese and base form generation for various languages. Additionally, it touches on companies offering premium NLP models, the significance of continuous model updates, and utilizing language models like GPT for prototyping enhanced through transfer learning within spaCy pipelines.