What is the difference between rule-based and ML-based NLP?

The major distinction between machine learning (ML) and rule-based natural language processing (NLP) is the interpretation and classification of language data. Rule-based NLP is based on human-written linguistic rules, which are explicitly programmed to analyze text, and ML-based NLP is based on statistical models that learn language patterns based on a large amount of data, enabling it to better understand the context, subtleties and changing language.

Rule-based NLP

• Bases itself on handcrafted rules and patterns which experts come up with specifying how language is to be processed and categorized.
• It is not taught on the basis of any information but on the basis of the predetermined set of linguistic and syntactic rules, including the ability to identify synonyms or certain structures of phrases.
• This is particularly effective in strict, predetermined and fails in adverse ambiguity, new vocabulary, and in highly ambiguous situations with which it was not formally programmed.
• The completeness and quality of the rule set is fundamental to accuracy, and can be costly to develop and maintain by large or dynamic domains of language.

Machine Learning-based NLP

• Based on annotated data, uses algorithms to generalize language patterns and meanings, and refines with time.
• It is able to discern context and accommodate misspelling, new words and subtleties of language without being told what to do explicitly by a human being.
• Compared to rule systems, ML-based NLP is more precise and flexible in handling large amounts of various and multidimensional text.
• It constantly refines and retrains its knowledge on new information, and is thus appropriate in dynamic contexts such as ticket tagging in customer support or conversational agents.

Therefore, whereas rule-driven rule-based NLP is static, data-driven ML-based NLP is more accurate, granular, and flexible in providing language understanding in many practical systems of the real world.