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# Towards the Annotation of Penn TreeBank with Information Structure

**Authors:** [Bernd Bohnet](https://md.mova.elamur.ai/free/en/docs/papers/authors/bernd-bohnet/), [Alicia Burga](https://md.mova.elamur.ai/free/en/docs/papers/authors/alicia-burga/), [Leo Wanner](https://md.mova.elamur.ai/free/en/docs/papers/authors/leo-wanner/) · **Year:** 2013 · **Venue:** Proceedings of the Sixth International Joint Conference on Natural Language Processing, pages 1250–1256 **Link:** [aclanthology.org/I13-1178](https://aclanthology.org/I13-1178/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**12** citations](https://openalex.org/works?filter=cites:W2251374677) openalex-snapshot · 2026-10-14[**4** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bohnet-2013-annotation-penn-treebank-information-structure/) of the 140673 papers whose bibliographies we read[Proceedings of the Sixth International Joint Conference on Natural Language Processing](https://aclanthology.org/volumes/I13-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/I13-1178/)
- [PDF](https://aclanthology.org/I13-1178.pdf)
- [OpenAlex](https://openalex.org/W2251374677)

**Authors (3)**

1. [Bernd Bohnet](https://openalex.org/A5066427994) [Semantic Scholar](https://www.semanticscholar.org/author/1678747) · h 26 · 4245 citations · 103 works
2. [Alicia Burga](https://openalex.org/A5088134995) [ORCID](https://orcid.org/0000-0001-6593-5681) · [Semantic Scholar](https://www.semanticscholar.org/author/2846241) · h 9 · 250 citations · 40 works
3. [Leo Wanner](https://openalex.org/A5087323271) [ORCID](https://orcid.org/0000-0002-9446-3748) · [Semantic Scholar](https://www.semanticscholar.org/author/1758440) · h 27 · 2955 citations · 246 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Information structure (here the thematicity part of Meaning-Text Theory's communicative structure: Specifier, Theme, Rheme, nested) is missing from the big English treebanks. The authors annotate 435 sentences of the CoNLL-2009 Penn Treebank with a manual procedure, using explicit criteria: fronted adjuncts are Specifiers, the subject is usually the Theme, negated or questioned material is Rheme, and relative pronouns in subject position are Themes. They compare a perceptron sequence tagger with a perceptron transition-based bracketing parser. On 35 test sentences (training on 360) the parser reaches 88.67% token accuracy, against 71.74% for the tagger; labelled bracket score 68.95 against 51.78. Errors are mainly consecutive Themes, consecutive Rhemes, and reduced clauses that are not labelled. Accuracy rises with the training size.

## Ideas

- I1. [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/) and `prag`: write the paper's criteria as deterministic rules over UD trees: `advmod`/`obl` fronted before the root as Specifier, `nsubj` as Theme, a negated or wh-questioned clause as Rheme. Measure token accuracy on a hand-checked sample of school sentences.
- I2. Add a shift-reduce bracketing step to the parse in `en/src/parse.rs` only for the communicative layer, with a rule-based oracle built from I1, so the learning curve can be measured against rules rather than against a learner.
- I3. Learning-curve measure: report accuracy for 50, 100, 200 and 360 training sentences, as the paper does, so the claim that small samples are enough is tested on Mova's data.

## For Mova

The chapter is [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/), with [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) for the constituents that count as Specifiers. The code is `en/src/lin.rs` (word order, where Theme comes first) and `prag/src/frames.rs`. Measure: token accuracy and bracket score for the rule version on a hand-checked sample, run twice. The paper's 435 sentences are not in the card; any test set must be built for Mova.

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{bohnet2013annotation,
  title = {{Towards the Annotation of Penn TreeBank with Information Structure}},
  author = {Bohnet, Bernd and Burga, Alicia and Wanner, Leo},
  booktitle = {Proceedings of the Sixth International Joint Conference on Natural Language Processing},
  year = {2013},
  url = {https://aclanthology.org/I13-1178/}
}
```

- [← previous Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions](https://md.mova.elamur.ai/free/en/docs/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
- [next → PropBank: Semantics of New Predicate Types](https://md.mova.elamur.ai/free/en/docs/papers/bonial-2014-propbank-semantics-new-predicate-types/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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