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# The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

**Authors:** [Mihai Surdeanu](https://md.mova.elamur.ai/free/en/docs/papers/authors/mihai-surdeanu/), [Richard Johansson](https://md.mova.elamur.ai/free/en/docs/papers/authors/richard-johansson/), [Adam Meyers](https://md.mova.elamur.ai/free/en/docs/papers/authors/adam-meyers/), [Lluís Màrquez](https://md.mova.elamur.ai/free/en/docs/papers/authors/lluis-marquez/), [Joakim Nivre](https://md.mova.elamur.ai/free/en/docs/papers/authors/joakim-nivre/) · **Year:** 2008 · **Venue:** CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning **Link:** [aclanthology.org/W08-2121](https://aclanthology.org/W08-2121/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only; evaluation only for the data

[**237** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/surdeanu-2008-conll-2008-shared-task-joint/) of the 140673 papers whose bibliographies we read[CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning](https://aclanthology.org/volumes/W08-21/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W08-2121/)
- [PDF](https://aclanthology.org/W08-2121.pdf)

**Authors (5)**

1. Mihai Surdeanu
2. Richard Johansson
3. Adam Meyers
4. Lluís Màrquez
5. Joakim Nivre

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

## What it is about

The 2008 CoNLL task joined syntactic dependency parsing and semantic role labelling in one dependency formalism. The English data convert the Penn Treebank constituents into dependencies, and the semantic layer covers both verbal predicates (PropBank) and nominal predicates (NomBank). Each system had to produce a syntactic dependency tree and the arguments of each predicate. The paper defines the subtasks, the closed and open challenges, and the evaluation measures: labelled attachment score, labelled F1 for the semantic dependencies, exact match of the whole sentence and perfect-proposition F1, which are much lower than the token scores. The main analysis is that nominal predicates are harder than verbal ones: the average gap in labelled F1 is 7.84 points on WSJ and Brown together, and 12.36 points on Brown alone, so nominal roles are more sensitive to domain change. Dividing the semantic F1 by the LAS gives a normalised score that isolates the role labeller from the parser. The paper also finds that wh-movement and split-clause links, which are non-projective, are picked out by simple patterns (a wh-word, a pair of commas).

## Ideas

- I1. Separate models for verbal and nominal predicates: a verb frame reader and a noun frame reader in `prag/src/roles.rs`, with their own feature lists. The paper's two best systems did this. Measure the gap between the two readers on the school texts, in the same way the paper measures it on WSJ and Brown.
- I2. Non-projective arcs by pattern: a wh-word at the start of a clause, or a comma pair around a clause, is a candidate for a non-projective link in `en/src/parse.rs`. Measure the precision and recall of that rule on UD EWT dev, with the baseline of no non-projective arcs, and run it twice.
- I3. A normalised role score for Mova: divide the labelled role F1 by the attachment score, as the paper does, so that a frame error is not blamed on a parse error. Report both numbers in the same table as `en parse-eval`.
- I4. Report exact match over whole sentences and perfect propositions (all roles of a predicate right at once) next to the token scores. The paper shows that these figures are much lower, and a lesson-level claim should be judged on them.

## For Mova

The chapters are [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/) (nominal predicates, relational nouns), [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (verbal predicates and argument structure), [25. Relative clauses](https://md.mova.elamur.ai/free/en/docs/languages/en/25-relative-clauses/) and [26. Questions](https://md.mova.elamur.ai/free/en/docs/languages/en/26-questions/) (wh-movement and the non-projective links). The code is `en/src/parse.rs` for the attachment and the non-projective rule, `prag/src/roles.rs` and `prag/src/frames.rs` for the role readers, and `en parse-eval` for the scores. The measures are the verb-noun gap on the school texts and the Early Modern texts, the non-projective precision and recall on UD EWT dev, and the normalised role score.

Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/)

## Cite

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

```
@inproceedings{surdeanu2008conll,
  title = {{The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies}},
  author = {Surdeanu, Mihai and Johansson, Richard and Meyers, Adam and Màrquez, Lluís and Nivre, Joakim},
  booktitle = {CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning},
  year = {2008},
  url = {https://aclanthology.org/W08-2121/}
}
```

- [← previous The role of Alternatives in the interpretation of scalars and numbers: Insights from the inference task](https://md.mova.elamur.ai/free/en/docs/papers/sun-2022-role-alternatives-interpretation-scalars-numbers/)
- [next → A Short Historical English Grammar](https://md.mova.elamur.ai/free/en/docs/papers/sweet-1892-short-historical-english-grammar/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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40. Zhao, S. 2005. Information Extraction from Multiple Syntactic Sources. Ph.D. thesis, NYU. [find](https://scholar.google.com/scholar?q=Zhao%2C+S%2E+2005%2E+Information+Extraction+from+Multiple+Syntactic+Sources%2E+Ph%2ED%2E+thesis%2C+NYU%2E)