Morphological Analysis and Generation › Morphological Analysis and Generation

In the previous chapter, you learned how languages build words from morphemes, whether by changing grammatical features or creating new words. This chapter turns that linguistic knowledge into computational machinery. We want systems that can do two things with morphologically complex words. Analysis takes a surface form like the Turkish word evlerinizden ("from your houses") and recovers its internal structure: stem ev, plural, possessive second person plural, ablative case. Generation runs the machine in reverse: given the stem ev and a bundle of grammatical features, produce the correct surface form.

This matters because a huge fraction of the world's languages pack what English does with separate words into single word forms. In Finnish, one inflected noun can mark number and case while also carrying possessive or clitic information that would take several English words to express. Systems that treat every word as an opaque atom, as the bag-of-words models from Part II did, drown in sparsity: thousands of distinct surface forms share a single stem, and a statistical model sees each of those forms too rarely to learn anything reliable about it. Solving morphology means recovering the shared structure underneath, so that evidence about one surface form transfers to the thousands of forms built on the same stem.

We will study this machinery through two dominant paradigms. First, finite-state morphology: hand-engineered transducers that encode lexicons and morphophonological rules as inspectable regular relations. Then neural morphology: character-level encoder-decoder models that learn inflection from examples. The code section implements compact rule-based demonstrations of the finite-state ideas and a small neural model that learns Turkish-style vowel harmony from synthetic data, then tests it on stems excluded from training. This lets us compare their assumptions and failure modes without pretending that one small experiment decides which paradigm wins in general.