Exploring the Spinal-STIG Model for Parsing French

Abstract

We evaluate statistical parsing of French using two probabilistic models derived from the Tree Adjoining Grammar framework: a Stochastic Tree Insertion Grammars model (STIG) and a specific instance of this formalism, called Spinal Tree Insertion Grammar model which exhibits interesting properties with regard to data sparseness issues common to small treebanks such as the Paris 7 French Treebank. Using David Chiang’s STIG parser (Chiang, 2003), we present results of various experiments we conducted to explore those models for French parsing. The grammar induction makes use of a head percolation table tailored for the French Treebank and which is provided in this paper. Using two evaluation metrics, we found that the parsing performance of a STIG model is tied to the size of the underlying Tree Insertion Grammar, with a more compact grammar, a spinal STIG, outperforming a genuine STIG. We finally note that a ""spinal"" framework seems to emerge in the literature. Indeed, the use of vertical grammars such as Spinal STIG instead of horizontal grammars such as PCFGs, afflicted with well known data sparseness issues, seems to be a promising path toward better parsing performance.

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