Evaluation of a Grammar of French Determiners

TL;DR

This paper evaluates the quality of a recursive transition network grammar for French determiners, demonstrating high precision and recall compared to an independently annotated corpus, and providing detailed syntactic information.

Contribution

It introduces an evaluation of a French determiner grammar based on recursive transition networks, highlighting its effectiveness over chunking and treebank data.

Findings

86% precision on determiner identification

92% recall on determiner identification

Provides deeper syntactic information than chunking or treebanks

Abstract

Existing syntactic grammars of natural languages, even with a far from complete coverage, are complex objects. Assessments of the quality of parts of such grammars are useful for the validation of their construction. We evaluated the quality of a grammar of French determiners that takes the form of a recursive transition network. The result of the application of this local grammar gives deeper syntactic information than chunking or information available in treebanks. We performed the evaluation by comparison with a corpus independently annotated with information on determiners. We obtained 86% precision and 92% recall on text not tagged for parts of speech.