---
title: "Specialized Models and Ranking for Coreference — Denis 2008"
description: "The paper looks at whether supervised coreference resolution can be improved without moving to complex joint or global inference."
authors: ["Pascal Denis", "Jason Baldridge"]
year: 2008
venue: "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D08-1069/"
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/denis-2008-specialized-models-ranking-coreference-resolution/"
---
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# Specialized Models and Ranking for Coreference Resolution

**Authors:** [Pascal Denis](https://md.research.elamur.ai/free/authors/pascal-denis/), [Jason Baldridge](https://md.research.elamur.ai/free/authors/jason-baldridge/) · **Year:** 2008 · **Venue:** Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing · **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/D08-1069/) · [PDF](https://aclanthology.org/D08-1069.pdf) · [OpenAlex](https://openalex.org/W2067982155)

**Citations:** 117 citations (openalex-snapshot · 2026-10-14) · [54](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper looks at whether supervised coreference resolution can be improved without moving to complex joint or global inference. Denis and Baldridge change the standard mention-pair classifier pipeline in two ways. First, they replace binary classification with a log-linear ranker, trained with TADM, that scores all candidate antecedents of a mention against each other. Second, they train separate models for different kinds of referring expressions, drawing on linguistic accessibility theory. Rankers always choose some antecedent, so a discourse-status (anaphoricity) classifier first decides which mentions should be sent to them. On the ACE corpus, the combined system (specialized rankers plus the discourse-status filter) beats an otherwise identical classifier baseline by more than 3 F-points on MUC, B³ and CEAF. The authors credit the gains to sharper antecedent selection and to a better division of labor across mention types. The work shows that simple, efficient local models can rival heavier global approaches. Oracle experiments point to the discourse-status component as the main remaining bottleneck.

## Key points
- The specialized models cover five mention types: third-person pronouns, speech pronouns, proper names, definite descriptions, and others. ACE has 19,322 training anaphors and 4,599 test anaphors.
- The specialized ranker system (RANK+DS+SP) improves on the standard classifier by 3.2 F-points on MUC, 3.1 on B³ and 3.6 on CEAF.
- The discourse-status classifier reaches 80.8% accuracy, compared with a 59.7% majority-class baseline.
- Wrong antecedents for true anaphors drop to 17.9% with RANK+DS+SP, against 23.1% for the plain CLASS baseline.
- Using perfect discourse status (an oracle) gives large further gains. With a perfect resolver but the real discourse-status classifier, F-scores are capped at 88.1 (MUC), 85.2 (B³) and 79.7 (CEAF).

*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: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/) · [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

54 papers of our library cite it (of the 140673 whose bibliographies we read); 31 of them have a page here, 23 not yet.

- [Antecedent Selection for Sluicing: Structure and Content](https://md.research.elamur.ai/free/papers/aclbulk-2016-anand-antecedent-selection-sluicing-structure-content/)
- [Unsupervised Event Coreference Resolution](https://md.research.elamur.ai/free/papers/bejan-2014-unsupervised-event-coreference-resolution/)
- [A Constrained Latent Variable Model for Coreference Resolution](https://md.research.elamur.ai/free/papers/chang-2013-constrained-latent-variable-model-coreference/)
- [Decentralized Entity-Level Modeling for Coreference Resolution](https://md.research.elamur.ai/free/papers/durrett-2013-decentralized-entity-level-modeling-coreference/)
- [Easy Victories and Uphill Battles in Coreference Resolution](https://md.research.elamur.ai/free/papers/durrett-2013-easy-victories-uphill-battles-coreference/)
- [A Joint Model for Entity Analysis: Coreference, Typing, and Linking](https://md.research.elamur.ai/free/papers/durrett-2014-joint-model-entity-analysis-coreference/)
- [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/papers/fernandes-2014-latent-trees-coreference-resolution/)
- [BERT for Coreference Resolution: Baselines and Analysis](https://md.research.elamur.ai/free/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
- [Coreference Resolution with Entity Equalization](https://md.research.elamur.ai/free/papers/kantor-2019-coreference-resolution-entity-equalization/)
- [Optimization in Coreference Resolution is not Needed: A Nearly-Optimal Algorithm with Intensional Constraints](https://md.research.elamur.ai/free/papers/klenner-2009-optimization-coreference-resolution-is-not/)
- [Bridging Resolution: A Survey of the State of the Art](https://md.research.elamur.ai/free/papers/kobayashi-2020-bridging-resolution-survey-state-art/)
- [Bridging Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/papers/kobayashi-2021-bridging-resolution-making-sense-state/)
- [Interpreting Anaphoric Shell Nouns using Antecedents of Cataphoric Shell Nouns as Training Data](https://md.research.elamur.ai/free/papers/kolhatkar-2013-interpreting-anaphoric-shell-nouns-antecedents/)
- [Rule-based Coreference Resolution in German Historic Novels](https://md.research.elamur.ai/free/papers/krug-2015-rule-based-coreference-resolution-german/)
- [Improving pairwise coreference models through feature space hierarchy learning](https://md.research.elamur.ai/free/papers/lassalle-2013-improving-pairwise-coreference-models-through/)
- [Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/papers/lu-2020-conundrums-entity-coreference-resolution-making/)
- [Prune-and-Score: Learning for Greedy Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2014-prune-score-learning-greedy-coreference/)
- [Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/)
- [Latent Structures for Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2015-latent-structures-coreference-resolution/)
- [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/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://md.research.elamur.ai/free/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/)
- [Supervised Models for Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2009-supervised-models-coreference-resolution/)
- [Coreference Resolution with World Knowledge](https://md.research.elamur.ai/free/papers/rahman-2011-coreference-resolution-world-knowledge/)
- [Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge](https://md.research.elamur.ai/free/papers/rahman-2012-resolving-complex-cases-definite-pronouns/)
- [Translation-Based Projection for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2012-translation-based-projection-multilingual-coreference/)
- [SemEval-2010 Task 1: Coreference Resolution in Multiple Languages](https://md.research.elamur.ai/free/papers/recasens-2009-semeval-2010-task-1-coreference/)
- [A Global Relaxation Labeling Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2010-global-relaxation-labeling-approach-coreference/)
- [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/)
- [Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution](https://md.research.elamur.ai/free/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/)
- [Learning Global Features for Coreference Resolution](https://md.research.elamur.ai/free/papers/wiseman-2016-learning-global-features-coreference-resolution/)

## Cite

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

```bibtex
@inproceedings{denis2008specialized,
  title = {{Specialized Models and Ranking for Coreference Resolution}},
  author = {Denis, Pascal and Baldridge, Jason},
  booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
  year = {2008},
  doi = {10.3115/1613715.1613797},
  url = {https://doi.org/10.3115/1613715.1613797}
}
```

- [← previous Soft Syntactic Constraints for Hierarchical Phrased-Based Translation](https://md.research.elamur.ai/free/papers/marton-2008-soft-syntactic-constraints-hierarchical-phrased/)
- [next → Statistical Anaphora Resolution in Biomedical Texts](https://md.research.elamur.ai/free/papers/gasperin-2008-statistical-anaphora-resolution-biomedical-texts/)
- [↑ back to papers of 2008](https://md.research.elamur.ai/free/papers/2008/)

## References

35 references, 10 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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