Morphology and Word Formation › Inflection versus Derivation

The two most important morphological processes share a mechanism (attach an affix) but serve very different purposes. Keeping them distinct is essential, because most NLP tools handle them differently, and conflating them causes subtle bugs. Lemmatizers, for instance, are built to undo inflection only; pointing one at a derived word and expecting it to recover the original root misunderstands both the tool and the linguistics.

Inflection creates different grammatical forms of the same lexeme. Inflected forms share the word's core meaning and part of speech; they differ only in grammatical features like number, tense, person, case, or gender. Think of inflection as changing the "settings" on a single lexical item rather than creating a new item altogether.

English inflection is famously sparse, but it exists:

Other languages inflect far more heavily. Spanish hablo, hablas, habla, hablamos, habláis, hablan are six forms of one verb, each marking person and number. Finnish nouns inflect for fifteen cases, and Turkish verbs chain together tense, aspect, mood, person, and number markers. We return to this variation shortly, because it has direct engineering consequences: the more inflection a language performs, the more surface forms any fixed vocabulary must either contain or fail on.

Even under generous assumptions about how many categories combine, the trend in @fig-paradigm-size-typology is clear: moving one step along the typological spectrum multiplies the number of forms a system must handle by orders of magnitude. This is the arithmetic behind every vocabulary-budget failure we examine below.

The defining property of inflection is paradigmatic regularity: all forms of a lexeme slot into the same grammatical environments. Replace walked with strolled in a sentence and the grammar still holds, because both are past-tense verb forms. Inflection never changes part of speech and rarely shifts meaning beyond grammatical content. This regularity is also what makes inflection tractable to model: because every lexeme of a given class takes essentially the same set of forms, a system that learns the pattern for one verb has largely learned it for all of them, modulo irregular exceptions like go/went.

Derivation creates new lexemes. Derived forms get their own dictionary entries because their meaning or word class departs from the base.

• happy (adjective) $\to$ unhappy (adjective, negated) $\to$ unhappiness (noun); unhappily (adverb) branches off unhappy via -ly
• nation (noun) $\to$ national (adjective) $\to$ nationalize (verb) $\to$ nationalization (noun again)

Notice the last chain: derivation can cycle a word back to its original part of speech while accumulating layers of meaning. Each derived form is a genuinely new lexical item, and some derivations are semantically idiosyncratic. Some -er derivatives are straightforwardly compositional agents (unionizer organizes workers into a union; killer kills), but the suffix is not uniformly agentive: a dinner is not an agent at all. These quirks matter for anyone building compositional models of meaning: if suffix meanings were fully predictable, deriving semantics from morphology would be trivial, and it is not.

Derivational affixes are also selective in ways inflectional affixes are not. -ness attaches to adjectives (kindness, darkness) but not to verbs; *runness is impossible. These selectional restrictions make derivation a knowledge-heavy process, which is why the next chapter treats morphological generation as a real modeling problem rather than a solved string operation. Knowing that -ness needs an adjective base is exactly the kind of structured knowledge that neither frequency counts nor character n-grams capture cleanly.

@fig-affix-selectivity-heatmap makes the asymmetry visible. The bottom two rows, representing inflection, are bright only in the relevant base class: plural -s applies productively to count nouns, while past -ed and progressive -ing apply productively to verbs. The derivational rows show different, often narrower selectional patterns. Productivity within a class is not the same as indiscriminate attachment across parts of speech.

In practice, ask three questions about an affixed form:

1. Does the part of speech change? quick $\to$ quickly changes adjective to adverb: derivation. walk $\to$ walked keeps it a verb: inflection.
2. Would the form get its own dictionary entry? Nationalize would; nationalizes would not.
3. Is the affix productive and paradigmatic? Nearly every English verb takes -ing; almost none take -ment. That asymmetry marks -ing as inflectional and -ment as derivational.

No single question is decisive on its own, since borderline cases exist where derivational affixes approach productivity or inflectional ones drift toward lexicalization. But asked together, they resolve the vast majority of cases correctly, and they encode precisely the properties our computational tools exploit: lemmatizers target the paradigmatic, part-of-speech-preserving changes, and leave everything else alone.

Allomorphy deserves special attention from NLP practitioners because it sits at the exact boundary between sound, spelling, and structure. The underlying morpheme is one thing; its written realization varies with the phonetic environment around it. Any pipeline that treats spelling changes as noise will systematically corrupt stems, and any evaluation of a stemmer or segmenter should check whether allomorphic repairs are being made, not just whether affixes were removed.