Lesson 01 promised that lemmatization needs a part-of-speech tag. Without knowing running is a verb, a lemmatizer cannot reduce it to run. Without knowing better is an adjective, it cannot reduce to good.
That promise hid a whole subfield. Part-of-speech tagging assigns grammatical categories. Syntactic parsing recovers the sentence's tree structure: which word modifies which, which verb governs which arguments. Classical NLP spent twenty years refining both. Then deep learning collapsed them into a token-classification task on top of a pretrained transformer, and the research community moved on.
Not the applied community. Every structured-extraction pipeline still uses POS and dependency trees under the hood. LLM-generated JSON gets validated against grammatical constraints. Question-answering systems decompose queries using dependency parses. Machine translation quality evaluators check alignment of parse trees.
Worth knowing. This lesson introduces the tagsets, the baselines, and the point where you stop implementing from scratch and call spaCy.
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