Dependency Syntax and Universal Dependencies › From Constituency to Dependency

A constituency tree groups words into nested phrases. For She gave him a book a small phrase-structure grammar might derive a sentence node S with two children, an NP and a VP. The NP contains the single word She. The VP contains a verb V, which contains gave, and two noun phrases. The first noun phrase contains him, and the second contains a book. Internal nodes include phrase categories (S, NP, VP) and a preterminal word category (V); leaves are words. The structure asserts that him and a book are separate noun phrases, that gave him a book is a verb phrase, and that the whole thing is a sentence.

The grouping records grammatical units. The constituency tree says that gave him a book behaves as a unit, that him and a book are two distinct noun phrases, and that She is outside the verb phrase. Those claims matter for agreement, movement, coordination, and any grammatical process that refers to phrases rather than individual words. Constituency trees also have the advantage of being directly compatible with context-free grammars, which come with a mature parsing toolkit.

Constituency has a long tradition in English annotation, including the Penn Treebank. Ordinary context-free grammars (CFGs) directly describe continuous phrase structure. They support a rich parsing toolkit, including Earley and CYK algorithms and probabilistic grammars.

But constituency analyses have drawbacks for cross-linguistic work and for downstream NLP:

• Phrase structure depends on the theory. Different theories can analyze the big dog as NP or DP, changing its internal structure and head choice.
• Flexible word order requires an ordering account. A constituency analysis must specify how it represents material that does not form one continuous surface span. Movement is one option; discontinuous constituents and separate ordering constraints are others. Flexible order does not by itself rule out constituency analysis.
• Long-distance relations require an explicit account. In Who do you think she saw? the object of saw appears at the front. Constituency frameworks may represent this relation with empty categories, nonlocal features such as SLASH, or other mechanisms.
• An application may need direct relations. Dependency paths can be useful for relation extraction. Semantic role labeling and coreference may also require phrase spans, semantic roles, or discourse context; their needs do not reduce to finding subjects.

These representation choices matter when selecting an analysis for multilingual or application-oriented work. A parser that has to decide whether a phrase is an NP or a DP must commit to a theoretical choice that may not transfer well across languages or annotation projects. A parser that records a subject relation can avoid that projection choice: it says that this noun is the subject of that verb.

Dependency syntax gives a compact way to record head-dependent relations directly. In a basic tree with $n$ syntactic words, there are $n$ word nodes and $n-1$ word-to-word arcs; including the artificial root adds one node and one arc. Phrase-category nodes are absent. This does not eliminate linguistic theory choices or make discontinuous constructions trivial, but it makes a chosen attachment, such as the iobj relation from gave to him, explicit.

The representations retain different information. A dependency subtree rooted at book can identify the words a book, so dependency trees do preserve some groupings. They do not explicitly store all phrase categories and intermediate projections from a constituency tree. Recovering a constituency analysis requires additional conventions; converting a constituency tree into dependencies likewise requires head-selection rules. The appropriate representation depends on the task and its annotation requirements.

The two representations emphasize different information:

• Constituency is optimized for grouping: it tells you which words form phrases and how those phrases nest.
• Dependency is optimized for linking: it tells you which word is the syntactic governor of which other word and what role the dependent plays.

Many sentences can be represented either way, but the representations are not informationally identical, and converting between them requires making decisions that one representation leaves implicit.