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# German and English Treebanks and Lexica for Tree-Adjoining Grammars

**Authors:** Miriam Kaeshammer, [Vera Demberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/vera-demberg/) · **Year:** 2012 · **Venue:** Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12) **Link:** [aclanthology.org/L12-1204](https://aclanthology.org/L12-1204/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only; nothing copied into a release. The resources depend on the licensed Penn Treebank and TIGER corpora: evaluation only.

[**4** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kaeshammer-2012-german-english-treebanks-lexica-tree/) of the 140673 papers whose bibliographies we read[Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)](https://aclanthology.org/volumes/L12-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/L12-1204/)
- [PDF](https://aclanthology.org/L12-1204.pdf)

**Authors (2)**

1. Miriam Kaeshammer
2. Vera Demberg

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper builds a treebank and a lexicon for PLTAG, an incremental, psycholinguistically motivated version of tree-adjoining grammar. The English side is converted from the Penn Treebank, the German side from TIGER. Conversion adds structure the original annotation lacks, so that modifiers can adjoin; German uses sister-adjunction because its free word order produces flat trees. Elementary trees are extracted from the converted trees, and prediction trees are derived from connection paths, so that a parser can predict upcoming structure word by word. The conversion yields complete correct trees for 97.6% of PTB trees and 99.8% of TIGER trees. The English lexicon has about 6,000 tree templates, with 2.37 trees per word on average; the German lexicon is larger because German predicts more.

## Ideas

- I1. `en/src/lin.rs` and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/): a table of multi-word entries with gaps, such as `either ... or` and particle verbs like `show up`, each stored with its positions. Measure how many gold multi-word units in UD EWT dev are recovered by the table.
- I2. Report ambiguity per lemma the way the paper does (trees per word, and the most ambiguous words such as `and`). Keep a count table in `en/src/dict.rs` order, and use it to choose which function words need a rule.
- I3. Prefix-level measure for `en/src/parse.rs`: after each word, count the attachments still open, and report the parser's accuracy on the prefix. This tests the paper's point that a predictive grammar needs fewer open decisions.

## For Mova

The chapters are [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) (multi-word units), [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (particle verbs) and [20. Syntax: the Universal Dependencies frame](https://md.mova.elamur.ai/free/en/docs/languages/en/20-syntax-overview/) (the parse). The code is `en/src/dict.rs`, `en/src/parse.rs` and `en/src/lin.rs`. The measures: recall of multi-word units on UD EWT dev, and per-lemma ambiguity counts. Only the ideas and the measures are for Mova; the treebanks themselves are not carried into the build.

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

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

```
@inproceedings{kaeshammer2012german,
  title = {{German and English Treebanks and Lexica for Tree-Adjoining Grammars}},
  author = {Kaeshammer, Miriam and Demberg, Vera},
  booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  url = {https://aclanthology.org/L12-1204/}
}
```

- [← previous Speech act attenuation in the history of English: The case of apologies](https://md.mova.elamur.ai/free/en/docs/papers/jucker-2019-speech-act-attenuation-history-english/)
- [next → Parsing Gapping Constructions Based on Grammatical and Semantic Roles](https://md.mova.elamur.ai/free/en/docs/papers/kato-2020-parsing-gapping-constructions-based-grammatical/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

30 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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