computational-linguistics
4 free lessons tagged computational-linguistics across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
What Neural Language Models Can Actually Compute
The same hierarchy that classifies grammars can classify neural networks. This lesson places recurrent networks and transformers on the Chomsky ladder, explains why a transformer is theoretically weaker than it looks, and shows how formal language theory predicts where today's language models generalize and where they break.
Morphology and Finite-State Methods
Words have internal structure, and modelling it is computational linguistics' cleanest success. This lesson covers morphemes and the ways languages build words, why the finite-state machines at the bottom of the hierarchy are enough, and how finite-state transducers analyze and generate word forms, up to the modern neural shared tasks.
Formal Grammars and the Chomsky Hierarchy
How much computational power does a language need? The Chomsky hierarchy answers it with four nested classes of grammar. This lesson builds the ladder from regular to recursively enumerable, shows the machine that recognizes each rung, and locates human language on it using the classic proof that it is not context-free.
The Science of Language, Computationally
Computational linguistics treats human language as something to be modelled precisely, and language fights back at every level. This lesson maps the levels of linguistic structure, shows why ambiguity, recursion, and Zipf's law make naive approaches fail, and frames the formal and statistical traditions the rest of the path builds on.