Fast Statistical Parsing with Parallel Multiple Context-Free Grammars
Paper in proceedings, 2014

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.

Author

Krasimir Angelov

University of Gothenburg

Peter Ljunglöf

University of Gothenburg

EACL'14, 14th Conference of the European Chapter of the Association for Computational Linguistics

Subject Categories

Language Technology (Computational Linguistics)

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Created

10/10/2017