Rule-Based Extraction
Rule-based extraction is a technique in natural language processing (NLP) and data mining that uses predefined patterns or rules to identify and extract specific information from unstructured text. It relies on linguistic rules, regular expressions, or domain-specific heuristics to locate entities, relationships, or facts. This approach is deterministic, meaning it produces consistent results based on the rules applied, without learning from data.
Developers should learn rule-based extraction when working on projects requiring high precision, interpretability, or when labeled training data is scarce. It is ideal for extracting structured data from documents like invoices, resumes, or legal texts, where patterns are well-defined and predictable. Use cases include information retrieval in business intelligence, automating data entry from forms, or building initial prototypes for NLP systems before transitioning to machine learning models.