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# Identifying Complex Phenomena in a Corpus via a Treebank Lens

**Authors:** [Dan Flickinger](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-flickinger/) · **Year:** 2006 · **Venue:** Proceedings of the 11th Annual Conference of the European Association for Machine Translation **Link:** [aclanthology.org/2006.eamt-1.16](https://aclanthology.org/2006.eamt-1.16/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**0** citations](https://openalex.org/works?filter=cites:W3100486171) openalex · 2026-10-19[Proceedings of the 11th Annual Conference of the European Association for Machine Translation](https://aclanthology.org/volumes/2006.eamt-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/2006.eamt-1.16/)
- [PDF](https://aclanthology.org/2006.eamt-1.16.pdf)
- [OpenAlex](https://openalex.org/W3100486171)

**Authors (1)**

1. [Flickinger, Dan](https://openalex.org/A5089106912) [ORCID](https://orcid.org/0000-0001-7959-999X) · [Semantic Scholar](https://www.semanticscholar.org/author/3209288) · h 32 · 5154 citations · 155 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper counts how often linguistic phenomena occur in a corpus by mapping the annotations of a Redwoods-style treebank to phenomena. The corpus is the 90,000-word English section of the LOGON development corpus. Of 6,407 items, 5,738 received at least one parse with the English Resource Grammar. For 650 of these no correct parse existed, which left 5,088 treebanked items. A phenomenon can map to one grammar rule, to a group of rules, to a lexical type, or to a configuration in the tree. The profiles are meant to set priorities for grammar work in the MT project, and a coverage table by token length shows how coverage varies with sentence length.

## Ideas

- I1. `en/src/explain.rs` with `en parse-eval`: label each EWT test arc with a phenomenon, for example an `obl` with a PP head as PP attachment and a `ccomp` as complementation, and report the error rate per label.
- I2. Group the labels by the structure of `languages/en/`, so that the profile lines up with the grammar chapters (clause types, relative clauses, coordination).
- I3. Frequency order: count the phenomena in EWT test and GUM test (GUM is test only), and order the examples of the next chapter by their frequency.

## For Mova

The code is `en/src/explain.rs`; the chapters are the ones under `languages/en/` for clauses and relative clauses (`21-clause-structure.md`, `25-relative-clauses.md`). The paper uses an ERG treebank, which is not UD, and the LOGON data are outside our licence list. Its coverage numbers are not English UD numbers.

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{flickinger2006identifying,
  title = {{Identifying Complex Phenomena in a Corpus via a Treebank Lens}},
  author = {Flickinger, Dan},
  booktitle = {Proceedings of the 11th Annual Conference of the European Association for Machine Translation},
  year = {2006},
  url = {https://aclanthology.org/2006.eamt-1.16/}
}
```

- [← previous Holistic Regression Testing for High-Quality MT: Some Methodological and Technological Reflections](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2005-oepen-holistic-regression-testing-high-quality/)
- [next → Computing relative polarity for textual inference](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2006-nairn-computing-relative-polarity-textual-inference/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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