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# Dependency-based Semantic Role Labeling of PropBank

**Authors:** [Richard Johansson](https://md.mova.elamur.ai/free/en/docs/papers/authors/richard-johansson/) and [Pierre Nugues](https://md.mova.elamur.ai/free/en/docs/papers/authors/pierre-nugues/) · **Year:** 2008 · **Venue:** Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing **Link:** [aclanthology.org/D08-1008](https://aclanthology.org/D08-1008/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**37** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/johansson-2008-dependency-based-semantic-role-labeling/) of the 140673 papers whose bibliographies we read[Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D08-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/D08-1008/)
- [PDF](https://aclanthology.org/D08-1008.pdf)

**Authors (2)**

1. Richard Johansson
2. Pierre Nugues

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

## What it is about

The paper builds a PropBank role labeler on top of a dependency parser. A syntactic model (second-order projective, with pseudo-projective handling of non-projective links) and a semantic model (a pipeline of logistic classifiers for sense, argument identification and labeling, followed by a global model) each produce candidates, and a joint reranker picks the complete syntactic-semantic output from the top 16 trees. Training uses the online passive-aggressive algorithm. On the CoNLL-2005 WSJ+Brown test set the segment-based F1 is 77.97 (78.84 after punctuation normalisation), close to the best published 79.22. On the CoNLL-2008 test set the dependency-based F1 is 84.29, above the constituent-based system used as reference (83.45). Precision drops sharply on the Brown out-of-domain set, which the authors trace to prepositional attachment errors of the parser.

## Ideas

- I1. Global constraints as deterministic filters on candidate frames: no core role twice, a continuation label C-X needs an X, and a relative-clause reference R-X needs an X. Implement them as checks in `prag/src/roles.rs`, count how many candidates each one removes, and measure dev argument F1 with and without them (run twice).
- I2. Keep a candidate pool of the top k structures from the rule-based labeler and rerank it with a log-linear score over the parts. Report dev F1 for k = 1, 4 and 16 to see where the reranker starts to pay.
- I3. Report the score in two ways, segment-based and head-based. The paper shows that the same output can rank differently under the two metrics, so a single number hides the choice of metric.
- I4. Keep an out-of-domain split in the school tests. The paper's Brown result shows that attachment accuracy falls off quickly out of domain, which a Mova lesson set can expose.

## For Mova

The chapters are [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/) and [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) (attachment), with [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) for argument alternations. The code is `prag/src/roles.rs` and `world/src/events.rs`. The measure is argument F1 on dev, with an out-of-domain check on the Early Modern lessons in `languages/en-x-emode/`, where the parser is expected to be weaker.

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{johansson2008dependency,
  title = {{Dependency-based Semantic Role Labeling of PropBank}},
  author = {Johansson, Richard and Nugues, Pierre},
  booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
  year = {2008},
  url = {https://aclanthology.org/D08-1008/}
}
```

- [← previous He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://md.mova.elamur.ai/free/en/docs/papers/jiang-2021-he-thinks-he-knows-better/)
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- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

31 references, 7 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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