Graphes param\'etr\'es et outils de lexicalisation
Eric Laporte (IGM-LabInfo), S\'ebastien Paumier (IGM-LabInfo)

TL;DR
This paper explores how lexicalizing a grammar with a parameterized-graph approach can reduce parsing errors and enhance syntactic parsing, demonstrated through experiments with a simple grammar and an informative French lexicon.
Contribution
It introduces a realistic model for grammar lexicalization using parameterized graphs and demonstrates its effectiveness in syntactic parsing.
Findings
Most lexicon-grammar information can be transferred into the grammar.
Lexicalization improves parsing accuracy.
The approach is effective with a simple formalism and rich lexicon.
Abstract
Shifting to a lexicalized grammar reduces the number of parsing errors and improves application results. However, such an operation affects a syntactic parser in all its aspects. One of our research objectives is to design a realistic model for grammar lexicalization. We carried out experiments for which we used a grammar with a very simple content and formalism, and a very informative syntactic lexicon, the lexicon-grammar of French elaborated by the LADL. Lexicalization was performed by applying the parameterized-graph approach. Our results tend to show that most information in the lexicon-grammar can be transferred into a grammar and exploited successfully for the syntactic parsing of sentences.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Linguistics and Discourse Analysis · Text Readability and Simplification
