Compacting the Penn Treebank Grammar

TL;DR

This paper investigates the size and completeness of the Penn Treebank grammar, proposing a rule compaction method that significantly reduces grammar size while maintaining parsing performance, highlighting the potential for more efficient grammar representations.

Contribution

The paper introduces a rule compaction algorithm for PTB grammar that reduces size and approaches a limit, with an enhanced probabilistic version improving linguistic plausibility.

Findings

Compacted grammar size approaches a limit with reduced redundancy.

Rule probability-based compaction maintains parsing performance.

Significant grammar size reduction (up to 69%) achieved with minimal performance loss.

Abstract

Treebanks, such as the Penn Treebank (PTB), offer a simple approach to obtaining a broad coverage grammar: one can simply read the grammar off the parse trees in the treebank. While such a grammar is easy to obtain, a square-root rate of growth of the rule set with corpus size suggests that the derived grammar is far from complete and that much more treebanked text would be required to obtain a complete grammar, if one exists at some limit. However, we offer an alternative explanation in terms of the underspecification of structures within the treebank. This hypothesis is explored by applying an algorithm to compact the derived grammar by eliminating redundant rules -- rules whose right hand sides can be parsed by other rules. The size of the resulting compacted grammar, which is significantly less than that of the full treebank grammar, is shown to approach a limit. However, such a compacted grammar does not yield very good performance figures. A version of the compaction algorithm taking rule probabilities into account is proposed, which is argued to be more linguistically motivated. Combined with simple thresholding, this method can be used to give a 58% reduction in grammar size without significant change in parsing performance, and can produce a 69% reduction with some gain in recall, but a loss in precision.