NLP / classical-nlp

Classical NLP

Tokenization, normalization, n-grams, BoW, TF-IDF, edit distance, Naive Bayes, topic modeling, NER, POS, and parsing.

shellbackend needed later
Focus
  • preprocessing
  • BoW vs TF-IDF
  • language modeling
  • topic modeling
Future feature idea

Text preprocessing and TF-IDF lab with n-grams, edit distance, Naive Bayes, and topic modeling.

Self-check
  1. What is the core job of "Classical NLP"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.