Supertagging-based Parsing with Linear Context-free Rewriting Systems

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

OriginalspracheEnglisch
TitelProceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Seiten2923-2935
Seitenumfang13
ISBN (elektronisch)9781954085466
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa

Externe IDs

Scopus 85117015034

Schlagworte

Schlagwörter

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