Grammar

In the UNL framework, a grammar is a set of rules that are used to generate UNL out of natural language, and natural language out of UNL. Along with the UNL<->NL dictionaries, they constitute the basic resource for UNLization and NLization.

Types of grammar

In the UNL framework there are three types of grammar:

• N-Grammar, or Normalization Grammar, is a set of N-rules used to prepare the natural language input for processing.
• T-Grammar, or Transformation Grammar, is a set of T-rules used to transform to transform natural language into UNL or UNL into natural language.
• D-Grammar, or Disambiguation Grammar, is a set of D-rules used to to improve the performance of transformation rules by constraining or forcing their applicability. The Disambiguation Rules follows the formalism:

Direction

In the UNL framework, grammars are not bidirectional, although they share the same syntax:

• The N-Grammar constains the normalization rules for natural natural analysis
• The UNL>NL T-Grammar contains the transformation rules used for natural language generation
• The UNL>NL D-Grammar contains the disambiguation rules used for improving the results of the UNL-NL T-Grammar
• The NL>UNL T-Grammar contains the transformation rules used for natural language analysis
• The NL>UNL D-Grammar contains the disambiguation rules used for tokenization and for improving the results of the NL-UNL T-Grammar

Units

In the UNL framework, grammars may target different processing units:

• Text-driven grammars process the source document as a single unit (i.e., without any internal subdivision)
• Sentence-driven grammars process each sentence or graph separately
• Word-driven grammars process words in isolation

Text-driven grammars are normally used in summarization and simplification, when the rhetorical structure of the source document is important. Sentence-driven grammars are used mostly in translation, when the source document can be treated as a list of non-semantically related units, to be processed one at a time. Word-driven grammars are used in information retrieval and opinion mining, when each word or node can be treated in isolation.
All these grammars share the same type of rule.

Recall

Grammars may target the whole source document or only parts of it (e.g. main clauses):

• Chunk grammars target only a part of the source document
• Full grammars target the whole source document

Precision

Grammars may target the deep or the surface structure of the source document:

• Deep grammars focus on the deep dependency relations of the source document and normally have three levels (network, tree and list)
• Shallow grammars focus only on the surface dependency relations of the source document and normally have only two levels (network and list)

Assessment

Main article: F-measure

Grammars are evaluated through a weighted average of precision and recall.