In computational linguistics, which structure consists of named nodes and labeled arcs and is commonly used to represent natural language grammars for parsing (e.g., Augmented Transition Networks)?

Introduction / Context:
Natural language understanding hinges on representing grammar so that a parser can recognize valid sentences. One influential approach models grammar as a set of states connected by labeled transitions that correspond to syntactic conditions. Knowing this representation and its variants (such as Augmented Transition Networks, ATNs) is foundational for students of NLP and AI.

Given Data / Assumptions:

• We need a network model with named nodes (states) and labeled arcs (transitions).
• The model should support parsing and grammar representation, not just data visualization.
• Examples include finite-state machines with augmentations for recursion and features.

Concept / Approach:

A Transition Network is a directed graph in which nodes represent parsing states and arcs are labeled with tests or grammar symbols. An Augmented Transition Network extends this with registers and procedures to handle nested structures and long-distance dependencies, making it more powerful than simple finite-state machines while remaining graph-based and operationally intuitive for parsing.

Step-by-Step Solution:

Verification / Alternative check:

Classic NLP literature describes ATNs exactly in these terms, with examples of arcs labeled by categories like NP, VP, or feature tests.

Why Other Options Are Wrong:

Tree Network: Trees are parse outputs, not the control network used to drive the parser.

Star/Complete Network: Graph topologies with no inherent linguistic semantics.

None: Incorrect because Transition Network fits precisely.

Common Pitfalls:

Confusing parse trees (results) with the network representation of the parsing procedure itself.

Final Answer:

Transition Network