---
title: "Data-Driven Parsing using Probabilistic Linear Context-Free"
description: "The authors describe the procedure for inducing an LCFRS from treebank data, binarizing it and… 73 references, cited by 31 papers here."
authors: ["Laura Kallmeyer", "Wolfgang Maier"]
year: 2013
venue: "Computational Linguistics"
source: "https://aclanthology.org/J13-1006/"
doi: "10.1162/coli_a_00136"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kallmeyer-2013-data-driven-parsing-probabilistic-linear/"
---
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# Data-Driven Parsing using Probabilistic Linear Context-Free Rewriting Systems

**Authors:** [Laura Kallmeyer](https://md.research.elamur.ai/free/en/authors/laura-kallmeyer/) and [Wolfgang Maier](https://md.research.elamur.ai/free/en/authors/wolfgang-maier/) · **Year:** 2013 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2013](https://md.research.elamur.ai/free/en/venues/cl/2013/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/J13-1006/) · [PDF](https://aclanthology.org/J13-1006.pdf) · [DOI 10.1162/coli\_a\_00136](https://doi.org/10.1162/coli_a_00136) · [OpenAlex](https://openalex.org/W2004967523)

**Citations:** 52 citations (OpenAlex, 2026-10-14) · [31](https://md.research.elamur.ai/free/en/papers/kallmeyer-2013-data-driven-parsing-probabilistic-linear/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Some constituency treebanks, notably the German NeGra and TIGER, annotate discontinuous constituents with crossing branches. Standard PCFG parsers cannot represent these structures, so they are usually removed by tree transformations that are generally irreversible and discard information. Kallmeyer and Maier address this by parsing directly with Probabilistic Linear Context-Free Rewriting Systems (PLCFRS). This extension of CFG lets a non-terminal cover a tuple of possibly non-adjacent strings. The authors describe the procedure for inducing an LCFRS from treebank data, binarizing it and refining the grammar. They then build a weighted deductive CYK parser. To keep parsing time manageable, they introduce several context-summary estimates of parse items. Some act as figures-of-merit for best-first search, and others support A* parsing. Experiments with grammars induced from NeGra show that discontinuity-aware data-driven parsing is practical and gives output of competitive quality. The parser outputs trees with crossing branches without assuming positions for traces. The authors describe it as the first parser for the whole PLCFRS class used successfully in data-driven parsing.

## Key points
- Discontinuity is frequent: roughly 25% of NeGra and TIGER sentences contain crossing branches, and about 20% of Penn Treebank sentences involve comparable structures.
- The method combines a binarized PLCFRS extracted from the NeGra treebank with a weighted deductive CYK parser. Grammar refinement includes a relative clause split.
- Several outside (context-summary) estimates are proposed, including the LN estimate. Some serve as best-first figures-of-merit, and others allow A* parsing.
- The authors decline a direct comparison with non-projective dependency parsers, partly because converting output to dependencies would need heuristic head finding. They contrast their approach with van Cranenburgh (2012), whose coarse-to-fine method is faster (sentences up to 40 words) but scores below standard PLCFRS parsing.
- Future work includes:
  - lowering complexity by restricting ill-nested rules or reducing fan-out;
  - relaxing the independence between same-category non-terminals with different fan-outs (e.g., VP with no gaps vs. VP with one gap);
  - evaluating on long-distance dependencies and without gold POS tags.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Heinrich Heine University Düsseldorf](https://md.research.elamur.ai/free/en/universities/heinrich-heine-university-dusseldorf/) — University, Düsseldorf, Germany · 33 works · 1,487 citations

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## Cite

Cite as: Laura Kallmeyer and Wolfgang Maier. 2013. Data-Driven Parsing using Probabilistic Linear Context-Free Rewriting Systems. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{kallmeyer2013data,
  title = {{Data-Driven Parsing using Probabilistic Linear Context-Free Rewriting Systems}},
  author = {Laura Kallmeyer and Wolfgang Maier},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00136},
  url = {https://doi.org/10.1162/coli_a_00136}
}
```

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*Updated 29 October 2026*