Grammars and Constituency Structure › Why Constituency?

A constituent is a span of words that behaves as a unit. Some groups of words can be replaced, moved, coordinated, or used as answers, while other groups cannot. In the continuous tree model used here, a constituent occupies a contiguous stretch of the sentence, with a particular order and boundaries. The phrase "the small dog" is a constituent because it occupies a continuous region and behaves as a single syntactic object. The words "the" and "dog" are also constituents, but they are smaller ones. Constituency is hierarchical because constituents can contain other constituents.

Consider the sentence:

The small dog barked.

The span "the small dog" behaves like a unit. It passes several constituency tests:

• Replacement: It can be replaced with a pronoun: "It barked."
• Coordination: It can be coordinated: "The small dog and the cat barked."
• Clefting: It can be moved into a cleft construction: "It was the small dog that barked."
• Answer: It can be used as an answer: "Who barked? The small dog."

By contrast, the span "small dog barked" does not behave like a unit in the same way. You cannot naturally replace it with a single pronoun or coordinate it with another span of the same kind. This suggests that "the small dog" is a constituent, while "small dog barked" is not. Constituency makes a testable claim about which spans pattern together under grammatical operations.

These constituency tests are useful diagnostics, but they are not mathematical proofs. Different tests can disagree, and some constructions are awkward. Replacement with a pronoun works cleanly for many noun phrases, but not for every phrase type. Some analyses allow coordination of nonconstituents, so use more than one diagnostic when arguing for a phrase structure. For NLP, constituency is a hypothesis about hierarchical organization, and a grammar is a compact way to state that hypothesis. The hypothesis may be refined or replaced, but it gives the parser something precise to compute with.

Constituency is not the only way to represent syntax. Dependency grammar, which we will meet in the next chapter, represents syntactic structure as links between words rather than nested phrases. The two views are related but not identical. A constituency tree emphasizes phrases and their internal composition. A dependency tree emphasizes head-dependent relations. For now, we focus on the phrase-structure view because it gives the cleanest introduction to formal grammars and chart parsing. It also makes recursion visually obvious: a phrase node can contain another phrase node of the same type, and that containment is exactly what a CFG rule describes.

It also helps to keep four things distinct:

• Tokens are the surface words or word pieces, such as "the", "dog", and "chased".
• Preterminal categories are labels attached to individual tokens, such as Det, N, and V. These are closely related to the POS tags from the previous part.
• Phrasal categories are labels for constituents, such as NP, VP, and PP.
• The sentence root is the largest constituent, usually S, which dominates the entire token sequence.

In the nonempty lexicalized examples here, a parse tree connects all four. Its leaves are tokens, its lowest internal nodes are preterminal categories, its higher internal nodes are phrases, and its root is the sentence category. This layering is important because it separates lexical information from structural information. The token "dog" is a string. The preterminal N says that this string is functioning as a noun here. The phrasal node NP says that a larger span is functioning as a noun phrase. The root S says that the whole span is a sentence. A complete constituency parse connects these layers; a parser may infer them jointly or take some, such as tokens and POS tags, as input.