---
title: "Semantic Role Labeling for Coreference — Ponzetto 2006"
description: "Most machine-learning coreference resolvers depend on surface cues such as string overlap, sentence distance and grammatical form, so they often fail…"
authors: ["Simone Paolo Ponzetto", "Michael Strube"]
year: 2006
venue: "Demonstrations"
source: "https://aclanthology.org/E06-2015/"
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/papers/ponzetto-2006-semantic-role-labeling-coreference-resolution/"
---
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# Semantic Role Labeling for Coreference Resolution

**Authors:** [Simone Paolo Ponzetto](https://md.research.elamur.ai/free/authors/simone-paolo-ponzetto/), [Michael Strube](https://md.research.elamur.ai/free/authors/michael-strube/) · **Year:** 2006 · **Venue:** Demonstrations · **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/E06-2015/) · [PDF](https://aclanthology.org/E06-2015.pdf) · [OpenAlex](https://openalex.org/W1986400042)

**Citations:** 34 citations (openalex-snapshot · 2026-10-14) · [11](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most machine-learning coreference resolvers depend on surface cues such as string overlap, sentence distance and grammatical form, so they often fail when linking pronouns or common nouns requires semantic knowledge. Ponzetto and Strube test whether automatically produced semantic role labels can fill this gap. They reimplemented the Soon et al. (2001) pairwise system with 12 features and a Maximum Entropy classifier. They then ran the ASSERT parser, an SVM-based tagger that outputs PropBank arguments, and added two features: the argument–predicate pairs of each mention in a candidate pair (I SEMROLE, J SEMROLE).

The system was prototyped on MUC-6/MUC-7 and evaluated on the ACE 2003 training corpus (BNEWS and NWIRE, split 60/20/20). Adding SRL raised the MUC F1 on merged ACE data from 67.3 to 68.8, mainly through better recall. The largest gains were for pronouns and common nouns, while proper names saw no benefit. Feature analysis ranked the SRL features just below string matching and alias. The study shows that shallow event-level semantics can help coreference where morpho-syntactic features are insufficient.

## Key points
- The duplicated Soon baseline matched or exceeded the original system on MUC. On MUC-6 F1 was 65.3 versus 62.3, and on MUC-7 it was 61.1 versus 60.4.
- On ACE 2003 merged data, adding SRL changed the scores as follows:
  - Recall rose from 54.5 to 56.4, and precision went from 88.0 to 88.2.
  - Pronoun accuracy rose from 34.7 to 40.3, and common-noun accuracy from 20.4 to 22.0.
  - Proper-name accuracy fell from 53.1 to 52.1.
- F1 also improved within each section: BNEWS went from 60.6 to 64.0, and NWIRE from 69.0 to 69.8.
- ASSERT labels covered 11,406 of 32,502 extracted noun phrases, using 2,801 distinct argument–predicate pairs. A label was attached to a mention only when the argument and the noun phrase shared a head.
- In the ablation, removing both SEMROLE features cost 1.50 F1, the 5th-largest drop of the 12 removals listed. Removing the noisy WN CLASS feature cost only 0.56 F1.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) · [Semantic role labeling](https://md.research.elamur.ai/free/topics/semantic-role-labeling/)

## Cited by

Cited by

11 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 6 not yet.

- [Narrative Schema as World Knowledge for Coreference Resolution](https://md.research.elamur.ai/free/papers/irwin-2011-narrative-schema-as-world-knowledge/)
- [Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective](https://md.research.elamur.ai/free/papers/kong-2009-employing-centering-theory-pronoun-resolution/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes](https://md.research.elamur.ai/free/papers/pradhan-2011-conll-2011-shared-task-modeling/)
- [CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes](https://md.research.elamur.ai/free/papers/pradhan-2012-conll-2012-shared-task-modeling/)

## Cite

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

```bibtex
@inproceedings{ponzetto2006semantic,
  title = {{Semantic Role Labeling for Coreference Resolution}},
  author = {Ponzetto, Simone Paolo and Strube, Michael},
  booktitle = {Demonstrations},
  year = {2006},
  doi = {10.3115/1608974.1608993},
  url = {https://doi.org/10.3115/1608974.1608993}
}
```

- [← previous Scalable Inference and Training of Context-Rich Syntactic Translation Models](https://md.research.elamur.ai/free/papers/galley-2006-scalable-inference-training-context-rich/)
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- [↑ back to papers of 2006](https://md.research.elamur.ai/free/papers/2006/)

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