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Statistical parsing models have recently been proposed that employ a bounded stack in time-series (left-to-right) recognition, using a right-corner transform defined over training trees to minimize stack use (Schuler et al., 2008). Corpus results have shown that a vast majority of naturally-occurring sentences can be parsed in this way using a very small stack bound of three to four elements. This suggests that the standard cubic-time CKY chart-parsing algorithm, which implicitly assumes an unbounded stack, may be wasting probability mass on trees whose complexity is beyond human recognition or generation capacity. This paper first describes a version of the right-corner transform that is defined over entire probabilistic grammars (cast as infinite sets of generable trees), in order to ensure a fair comparison between bounded-stack and unbounded PCFG parsing using a common underlying model; then it presents experimental results that show a bounded-stack right-corner parser using a transformed version of a grammar significantly outperforms an unbounded-stack CKY parser using the original grammar.

Positive results for parsing with a bounded stack using a model-based right-corner transform