---
title: "Event Extraction as Dependency Parsing — McClosky 2011"
description: "The paper addresses biomedical event extraction, where events can take other events as… 53.5% F1. 29 references, cited by 73 papers here."
authors: ["David McClosky", "Mihai Surdeanu", "Christopher Manning"]
year: 2011
venue: "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/P11-1163/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mcclosky-2011-event-extraction-as-dependency-parsing/"
---
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# Event Extraction as Dependency Parsing

**Authors:** [David McClosky](https://md.research.elamur.ai/free/en/authors/david-mcclosky/), [Mihai Surdeanu](https://md.research.elamur.ai/free/en/authors/mihai-surdeanu/) and [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) · **Year:** 2011 · **Venue:** [Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2011](https://md.research.elamur.ai/free/en/venues/acl/2011/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P11-1163/) · [PDF](https://aclanthology.org/P11-1163.pdf) · [OpenAlex](https://openalex.org/W2102563561)

**Citations:** 163 citations (OpenAlex, 2026-10-14) · [73](https://md.research.elamur.ai/free/en/papers/mcclosky-2011-event-extraction-as-dependency-parsing/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses biomedical event extraction, where events can take other events as arguments and so form nested structures. Many earlier systems predict each event and argument separately. The authors instead treat the whole event-argument structure as a dependency tree. Event anchors and protein mentions become nodes, argument slots become arc labels, and a virtual ROOT node sits on top. Several MSTParser instances with different decoders are trained on these semantic trees, not on syntax. Their n-best outputs are converted back to events and rescored by a reranker that uses global features and is tuned to the task metric. A separate classifier predicts the anchors. The authors evaluate on the BioNLP'09 shared task, where the system reaches 53.5% F1 on development and 48.6% on test. These scores are competitive with top systems that rely on far more domain-specific engineering. The work shows that joint, structure-aware modeling helps, and that standard parsing machinery can serve as an event extractor that is largely domain-independent.

## Key points
- **Results:** The system scores 48.6% F1 on the BioNLP'09 test set and 53.5% on development. For comparison, the best shared-task system scored 52.0% and the best post-evaluation system 56.3%.
- **Effect of reranking:** Reranking across multiple decoders beats parsing without reranking in five of six configurations.
  - With predicted anchors, development F1 rises from 52.7 to 53.5.
  - With gold anchors, it rises from 72.7 to 73.1.
- **Ceiling from the tree conversion:** The conversion itself caps performance at 87.1 F1. The main losses come from:
  - removing cross-sentence events, about 5% of the corpus;
  - keeping only the first event when an argument is shared, which removes 4.7% of training events.
- **Oracle reranking:** Concatenating the n-best lists of three decoders gives an oracle of 86.7 with gold anchors, only 0.4 below the conversion limit. With predicted anchors the oracle is 73.4.
- **Simple vs. complex events:** Simple events are much easier than complex ones on test. PHOSPHORYLATION reaches 81.2 F1, while REGULATION reaches only 28.3.

## Interesting ideas
- **Use a parser as a structured predictor.** Any nested, anchor-based annotation can be recast as a labeled dependency tree, so a ready-made dependency parser can predict it. This could transfer to other nested semantic tasks without building a custom joint model.
- **Make the conversion reversible and measure its loss.** Lossy heuristics turn graphs into trees, and paired heuristics restore duplicated events afterward. Scoring the round trip on gold data gives an explicit ceiling, which is good practice whenever a task is squeezed into a simpler representation.
- **Diversify candidates, then rerank.** Pooling n-best lists from different decoders and choosing with a reranker trained on the task metric lets global, whole-structure features shape the final decision. Oracle scores show how much headroom the candidate pool leaves.
- **Diagnose components with gold substitution.** Swapping gold data into each pipeline stage in turn (anchors, parse, reranking, conversion) shows where errors originate. The method is cheap and applies to any multi-stage pipeline.
- **Extend the tree to whole documents.** A single document-level ROOT could keep cross-sentence events and allow discourse-level features. The same trick could help other tasks with sentence-crossing relations.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: David McClosky, Mihai Surdeanu and Christopher Manning. 2011. Event Extraction as Dependency Parsing. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.

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

```bibtex
@inproceedings{mcclosky2011event,
  title = {{Event Extraction as Dependency Parsing}},
  author = {David McClosky, Mihai Surdeanu and Christopher Manning},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1163/}
}
```

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## References

29 references, 12 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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*Updated 24 October 2026*