Rule-Based Text Processing
Rule-based text processing is a computational approach that uses predefined rules, patterns, or heuristics to analyze, extract, or manipulate textual data. It involves creating explicit instructions (e.g., regular expressions, keyword matching, or grammatical rules) to identify specific structures or content in text. This method is often contrasted with machine learning-based approaches, relying on human-crafted logic rather than learned patterns from data.
Developers should learn rule-based text processing for tasks requiring high precision, interpretability, and control, such as data validation, simple parsing, or when labeled training data is scarce. It is particularly useful in domains like log file analysis, basic natural language processing (e.g., named entity recognition with fixed patterns), or text cleaning in data pipelines, where rules can be clearly defined and performance needs to be deterministic.