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# Discriminant-Based MRS Banking

**Authors:** [Stephan Oepen](https://md.mova.elamur.ai/free/en/docs/papers/authors/stephan-oepen/) and [Jan Tore Lønning](https://md.mova.elamur.ai/free/en/docs/papers/authors/jan-tore-lonning/) · **Year:** 2006 · **Venue:** Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06) **Link:** [aclanthology.org/L06-1214](https://aclanthology.org/L06-1214/) · **Licence:** copyrighted (ELRA, all rights reserved; listed as [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) by the ACL Anthology) — summary only

[**80** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lingbulk-2006-oepen-discriminant-based-mrs-banking/) of the 140673 papers whose bibliographies we read[Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06)](https://aclanthology.org/volumes/L06-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/L06-1214/)
- [PDF](https://aclanthology.org/L06-1214.pdf)

**Authors (2)**

1. Stephan Oepen
2. Jan Tore Lønning

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

## What it is about

The paper describes how to build a corpus in which every sentence is paired with a meaning representation (MRS). Sentences are parsed with a broad-coverage grammar, and the right analysis is then chosen by binary decisions. The decisions are on semantic discriminants, which are local properties of the semantic dependency graph. The paper defines three types: presence of a predicate, dependencies between predicates, and properties of a relation's main index. An annotator accepts or rejects each discriminant, and every decision removes analyses from the parse forest. The examples are in Norwegian and English. The paper gives no evaluation numbers.

## Ideas

- I1. `annot`: use the discriminant form as the review step for silver trees. The reviewer answers short binary questions about one tree (this attachment goes to the verb, this noun is plural) instead of editing a full tree. Log accepted and rejected choices, and measure reviewer time and agreement on 100 EWT sentences with two reviewers.
- I2. `en/src/explain.rs`: attach to each arc decision the binary property that decided it, so that an explanation lists the decided properties. Check on EWT test that every chosen arc has one.
- I3. Pin-down count: for each EWT test tree, count how many binary decisions would fix the tree among the parser's candidates. Report the distribution. This is test only.

## For Mova

The code is `annot` and `en/src/explain.rs`; the chapter for the clause decisions is [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/). The paper is NC, so it is summary only, and it has no numbers to reuse. Measure on EWT only.

## Cite

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

```
@inproceedings{oepen2006discriminant,
  title = {{Discriminant-Based MRS Banking}},
  author = {Oepen, Stephan and Lønning, Jan Tore},
  booktitle = {Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06)},
  year = {2006},
  url = {https://aclanthology.org/L06-1214/}
}
```

- [← previous Computing relative polarity for textual inference](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2006-nairn-computing-relative-polarity-textual-inference/)
- [next → Adapting WSJ-Trained Parsers to the British National Corpus Using In-Domain Self-Training](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2007-foster-adapting-wsj-trained-parsers-british/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

15 references, 3 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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10. Oepen, S., Dyvik, H., Lønning, J. T., Velldal, E., Beermann, D., Carroll, J., Flickinger, D., Hellan, L., Johannessen, J. B., Meurer, P., Nordgård, T., & Rosén, V. (2004). Som å kapp-ete med trollet? Towards MRS-based Norwegian – English Machine Translation. In Proceedings of the 10th International… [find](https://scholar.google.com/scholar?q=Oepen%2C+S%2E%2C+Dyvik%2C+H%2E%2C+L%C3%B8nning%2C+J%2E+T%2E%2C+Velldal%2C+E%2E%2C+Beermann%2C+D%2E%2C+Carroll%2C+J%2E%2C+Flickinger%2C+D%2E%2C+Hellan%2C+L%2E%2C+Johannessen%2C+J%2E+B%2E%2C+Meurer%2C+P%2E%2C+Nordg%C3%A5rd%2C+T%2E%2C+%26+Ros%C3%A9n%2C+V%2E+%282004%29%2E+Som+%C3%A5+kapp%2Dete+med+trollet%3F+)
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