Symbolic NLP
Symbolic NLP (Natural Language Processing) is an approach to language understanding that relies on explicit rules, formal grammars, and symbolic representations of knowledge, rather than statistical or neural models. It involves creating structured representations of language using logic, ontologies, and hand-crafted rules to parse and interpret text. This methodology was dominant in early NLP research and is still used in applications requiring precise control, interpretability, or integration with expert systems.
Developers should learn Symbolic NLP when working on tasks that demand high accuracy, transparency, and rule-based reasoning, such as in legal document analysis, medical coding, or domain-specific chatbots where errors are costly. It is particularly useful in scenarios with limited training data or when integrating NLP with knowledge bases and expert systems, as it allows for explicit control over language processing logic. This approach is also valuable for educational purposes to understand foundational NLP concepts before diving into statistical methods.