---
title: "Coreference-inspired Coherence Modeling — Elsner 2008"
description: "Local coherence models for sentence ordering usually treat a sentence as a bag of words or as entities with syntactic roles."
authors: ["Micha Elsner", "Eugene Charniak"]
year: 2008
venue: "Proceedings of ACL-08: HLT, Short Papers"
source: "https://aclanthology.org/P08-2011/"
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/elsner-2008-coreference-inspired-coherence-modeling/"
---
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# Coreference-inspired Coherence Modeling

**Authors:** [Micha Elsner](https://md.research.elamur.ai/free/authors/micha-elsner/), [Eugene Charniak](https://md.research.elamur.ai/free/authors/eugene-charniak/) · **Year:** 2008 · **Venue:** Proceedings of ACL-08: HLT, Short Papers · **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/P08-2011/) · [PDF](https://aclanthology.org/P08-2011.pdf) · [OpenAlex](https://openalex.org/W2159757335)

**Citations:** 60 citations (openalex-snapshot · 2026-10-14) · [24](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Local coherence models for sentence ordering usually treat a sentence as a bag of words or as entities with syntactic roles. They ignore how an entity is actually expressed. Elsner and Charniak bring two ideas from coreference research into coherence modeling.

The first is a maximum-entropy classifier that decides whether a noun phrase introduces a new referent or points back to a known one. It uses features adapted from Uryupina (2003), drops order-dependent cues, and relies on a "same head" approximation of coreference. The second is the probabilistic pronoun model of Ge et al. (1998), which scores pronoun–antecedent pairs using Hobbs distance, mention counts, and gender and number agreement.

Each model is multiplied with the entity grid of Lapata and Barzilay (2005). The authors test on Wall Street Journal documents using two tasks: telling an original document from a shuffled version (discrimination), and reinserting a removed sentence (insertion). On its own, the entity grid is stronger than either new model. The full combination, however, clearly beats the grid on both tasks. This shows that the form of referring expressions carries useful coherence signal.

## Key points
- The discourse-new classifier reaches 87.4% F-score on the MUC-7 formal test set, slightly above Uryupina's 86.9. Its training labels come from the "same head" heuristic, so no gold coreference annotation is needed.
- The evaluation uses Penn Treebank WSJ sections 14–24 (1004 documents). On the AIRPLANE corpus the approach was ineffective, because those texts contain few pronouns or discourse-new markers.
- Discrimination accuracy and F-score:
  - Entity Grid: 76.17 accuracy, 77.55 F
  - EGrid+Disc-New: 78.88 accuracy, 80.31 F
  - Combined (all three models): 79.60 accuracy, 81.02 F
- Insertion score rises from 19.57 for the entity grid to 22.98 for the combined model. All differences are significant under Wilcoxon's signed-rank test (p \< .001).
- The pronoun model alone is weak (55.77 discrimination accuracy), since a displaced pronoun often finds another plausible antecedent nearby. Even so, removing it significantly hurts the combined system.

*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/)

## Cited by

Cited by

24 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 20 not yet.

- [Discourse Coherence in the Wild: A Dataset, Evaluation and Methods](https://md.research.elamur.ai/free/papers/lai-2018-discourse-coherence-wild-dataset-evaluation/)
- [Neural Net Models of Open-domain Discourse Coherence](https://md.research.elamur.ai/free/papers/li-2017-neural-net-models-open-domain/)
- [Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers](https://md.research.elamur.ai/free/papers/moosavi-2018-linguistic-features-improve-generalization-capability/)
- [Lexical Chaining for Measuring Discourse Coherence Quality in Test-taker Essays](https://md.research.elamur.ai/free/papers/somasundaran-2014-lexical-chaining-measuring-discourse-coherence/)

## Cite

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

```bibtex
@inproceedings{elsner2008coreference,
  title = {{Coreference-inspired Coherence Modeling}},
  author = {Elsner, Micha and Charniak, Eugene},
  booktitle = {Proceedings of ACL-08: HLT, Short Papers},
  year = {2008},
  doi = {10.3115/1557690.1557702},
  url = {https://doi.org/10.3115/1557690.1557702}
}
```

- [← previous Coreference Systems Based on Kernels Methods](https://md.research.elamur.ai/free/papers/versley-2008-coreference-systems-based-kernels-methods/)
- [next → Dependency-based Semantic Role Labeling of PropBank](https://md.research.elamur.ai/free/papers/johansson-2008-dependency-based-semantic-role-labeling/)
- [↑ back to papers of 2008](https://md.research.elamur.ai/free/papers/2008/)

## References

23 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Regina Barzilay and Mirella Lapata. 2005. Modeling local coherence: an entity-based approach. In ACL 2005. [find](https://scholar.google.com/scholar?q=Regina+Barzilay+and+Mirella+Lapata%2E+2005%2E+Modeling+local+coherence%3A+an+entity%2Dbased+approach%2E+In+ACL+2005%2E)
2. Regina Barzilay and Lillian Lee. 2004. Catching the drift: Probabilistic content models, with applications to generation and summarization. In HLT-NAACL 2004, pages 113–120. [find](https://scholar.google.com/scholar?q=Regina+Barzilay+and+Lillian+Lee%2E+2004%2E+Catching+the+drift%3A+Probabilistic+content+models%2C+with+applications+to+generation+and+summarization%2E+In+HLT%2DNAACL+2004%2C+pages+113%E2%80%93120%2E)
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10. Barbara J. Grosz, Aravind K. Joshi, and Scott Weinstein. 1995. Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 21(2):203–225. [find](https://scholar.google.com/scholar?q=Barbara+J%2E+Grosz%2C+Aravind+K%2E+Joshi%2C+and+Scott+Weinstein%2E+1995%2E+Centering%3A+A+framework+for+modeling+the+local+coherence+of+discourse%2E+Computational+Linguistics%2C+21%282%29%3A203%E2%80%93225%2E) [our card](https://md.research.elamur.ai/free/papers/cl-1995-grosz-centering-framework-modeling-local-coherence/)
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