Traditional NLP
Traditional NLP refers to natural language processing techniques and models developed before the deep learning revolution, typically relying on rule-based systems, statistical methods, and classical machine learning algorithms. It focuses on tasks like part-of-speech tagging, named entity recognition, and sentiment analysis using approaches such as Hidden Markov Models, Conditional Random Fields, and bag-of-words representations. These methods often require extensive feature engineering and linguistic knowledge to process and analyze text data.
Developers should learn Traditional NLP when working on projects with limited data, need interpretable models, or require lightweight solutions without heavy computational resources. It's particularly useful for domain-specific applications where rule-based systems can be tailored with expert knowledge, such as in legal or medical text analysis, and for understanding foundational concepts that underpin modern NLP techniques.