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Natural Language Processing (NLP)
/Traditional NLP (Non-Neural Approaches)
/Rule-Based Systems

Rule-Based Systems

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Rule-Based Systems

Rule-based systems represent the earliest approach to NLP, using hand-crafted linguistic rules created by human experts to process language. These systems rely on explicit grammatical constraints, lexicons, and pattern-matching techniques to analyze text in a deterministic manner.

Grammar parsers decompose sentences into their syntactic structures, using formal representations like context-free grammars to identify subjects, verbs, objects, and their relationships. Expert systems combine extensive knowledge bases with inference engines to make decisions about text meaning based on predefined rules.

While labor-intensive to develop and difficult to scale across linguistic variations, rule-based approaches offer complete transparency in their decision-making process and can achieve high precision in controlled domains where rules are well-defined. They remain valuable in specialized applications like legal document processing and certain aspects of grammar checking where interpretability is paramount.