Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Abstract

We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars.We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.

Discontinuous Incremental Shift-reduce Parsing

“…For a typical treebank LCFRS (Maier and Søgaard, 2008), k ≈ 3, instead of k = 1 for PCFG. In order to improve on otherwise impractical parsing times, LCFRS chart parsers employ different strategies to speed up search : Kallmeyer and Maier (2013) use A * search; van Cranenburgh (2012) and van Cranenburgh and Bod (2013) use a coarse-to-fine strategy in combination with DataOriented Parsing; Angelov and Ljunglöf (2014) use a novel cost estimation to rank parser items. Maier et al (2012) apply a treebank transformation which limits the block degree and therewith also the parsing complexity.…”

Implementation of a

“…The implementation is available as a part of Rustomata, 5 a framework for weighted automata with storage written in the programming language Rust. We used the NeGra corpus (German newspaper articles, 20,602 sentences, 355,096 tokens; Skut et al, 1998) to compare our parser to Grammatical Framework (Angelov and Ljunglöf, 2014), rparse (Kallmeyer and Maier, 2013), and discodop (van Cranenburgh et al, 2016) with respect to parse time and accuracy. 6 Our experiments were conducted on defoliated trees, i.e.…”

LR Parsing for LCFRS

“…The parsing of LCFRS has received attention both on the symbolic and on the probabilistic side. Symbolic parsing strategies, such as CYK and Earley variants have been presented [20][21][22], as well as automaton-based parsing [23] and data-driven probabilistic parsing techniques [24][25][26]. To our knowledge, however, no LR strategy for LCFRS has so far been presented in the literature.…”