Supertagging-based Parsing with Linear Context-free Rewriting Systems

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Contributors

Abstract

We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constituents establish a new state of the art. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. We describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure that transforms lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is evaluated on the English Discontinuous Penn Treebank and the German treebanks Negra and Tiger.

Details

Original languageEnglish
Title of host publicationProceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Pages2923-2935
Number of pages13
ISBN (electronic)9781954085466
Publication statusPublished - 2021
Peer-reviewedYes

External IDs

Scopus 85117015034

Keywords

Keywords

  • discontinuous, parsing, supertagging, discontinuous, linear context-free rewriting systems, parsing, supertagging