# Supertagging-based Parsing with Linear Context-free Rewriting Systems

arXiv: https://arxiv.org/abs/2010.10238 · HTML: https://mlsanity.com/paper/2010.10238/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering](https://mlsanity.com/paper/2104.06239/) (2021)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [Exploiting Pre-trained Encoder-Decoder Transformers for Sequence-to-Sequence Constituent Parsing](https://mlsanity.com/paper/2605.13373/) (2026)

## Abstract (arXiv)

We present the first supertagging-based parser for LCFRS. It utilizes neural classifiers and tremendously outperforms previous LCFRS-based parsers in both accuracy and parsing speed. Moreover, our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constitutents are excellent. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. It is an adaptation of previous work by M"orbitz and Ruprecht (2020). We also describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure for the transformation of lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is implemented and evaluated on the English Discontinuous Penn Treebank and the German corpora NeGra and Tiger.