Rule-Based NLP
Rule-Based NLP is a methodology in natural language processing that relies on manually crafted rules, patterns, and linguistic knowledge to analyze and process text. It involves defining explicit instructions, such as regular expressions, grammar rules, or dictionaries, to extract information, classify text, or perform tasks like tokenization and named entity recognition. This approach contrasts with machine learning-based NLP, which learns patterns from data automatically.
Developers should learn Rule-Based NLP when working on tasks that require high precision, interpretability, and control over language processing, such as in domains with strict regulatory requirements or limited training data. It is particularly useful for applications like parsing structured documents, implementing domain-specific grammars, or building prototypes where explainability is critical, such as in legal or medical text analysis.