Classical NLP
Classical NLP refers to traditional, rule-based and statistical approaches to natural language processing that predate modern deep learning methods. It involves techniques like tokenization, part-of-speech tagging, parsing, and named entity recognition using algorithms such as Hidden Markov Models, Conditional Random Fields, and context-free grammars. These methods rely heavily on linguistic rules, handcrafted features, and statistical models trained on annotated corpora.
Developers should learn Classical NLP when working on projects with limited data, need for interpretability, or in domains where deep learning models are impractical due to computational constraints. It is particularly useful for tasks like text preprocessing, information extraction in legacy systems, and building lightweight applications where transparency and control over language rules are critical, such as in healthcare or legal document analysis.