---
title: "Supervised Models for Coreference Resolution — Rahman 2009"
description: "This paper addresses noun phrase coreference resolution, the task of grouping mentions in a text that point to the same entity."
authors: ["Altaf Rahman", "Vincent Ng"]
year: 2009
venue: "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D09-1101/"
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/rahman-2009-supervised-models-coreference-resolution/"
---
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# Supervised Models for Coreference Resolution

**Authors:** [Altaf Rahman](https://md.research.elamur.ai/free/authors/altaf-rahman/), [Vincent Ng](https://md.research.elamur.ai/free/authors/vincent-ng/) · **Year:** 2009 · **Venue:** Proceedings of the 2009 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/D09-1101/) · [PDF](https://aclanthology.org/D09-1101.pdf) · [OpenAlex](https://openalex.org/W4253336001)

**Citations:** 75 citations (openalex-snapshot · 2026-10-14) · [69](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This paper addresses noun phrase coreference resolution, the task of grouping mentions in a text that point to the same entity. Standard learned resolvers use mention-pair classifiers, which have two problems. They judge each candidate antecedent in isolation, without comparing it to the other candidates, and they only see features of two mentions at a time. Rahman and Ng propose a cluster-ranking model to fix both problems. For each new mention, a learned ranker scores all preceding, partially built coreference clusters and links the mention to the best one. This combines the competition between candidates that mention rankers capture with the cluster-level features of entity-mention models. The same framework can also learn discourse-new detection, meaning whether a mention starts a new entity, together with resolution. This avoids the errors that a separate pipeline detector passes on. The authors train SVM light models and test on ACE 2005, using both gold (true) and system-detected mentions. The joint cluster ranker beats mention-pair, entity-mention and mention-ranking baselines by a significant margin under MUC, CEAF and B3. The results suggest that choosing the right learning framework matters a great deal for coreference.

## Key points
- **Results on true mentions:** the joint cluster ranker reaches F-scores of 76.0 (MUC), 63.3 (CEAF) and 64.0 (B3). The strongest baseline, a joint mention ranker, scores 73.4, 61.6 and 62.9, and the mention-pair model scores 70.4, 54.3 and 58.0.
- **Results on system mentions:** the joint cluster ranker gets MUC F = 69.3 and CEAF F = 59.5, compared with 62.5 and 53.4 for the mention-pair baseline.
- **Joint vs. pipeline:** learning discourse-new detection jointly with resolution beats the pipeline setup for both rankers, mainly because precision goes up. In the pipeline setup, a separate SVM classifier with 26 features filters out discourse-new mentions first.
- **Entity-mention vs. mention-pair:** the entity-mention baseline did not perform measurably better than the mention-pair model. This matches what Luo et al. (2004) found earlier.
- **Background and setup:** the approach turns the heuristic, salience-based cluster scoring of Lappin and Leass (1994) into a learned model. All learners are SVM light with default parameters, and significance was tested with Approximate Randomization (p = 0.05).

*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

69 papers of our library cite it (of the 140673 whose bibliographies we read); 37 of them have a page here, 32 not yet.

- [Exploring the steps of Verb Phrase Ellipsis](https://md.research.elamur.ai/free/papers/aclbulk-2016-liu-exploring-steps-verb-phrase-ellipsis/)
- [Coreference Semantics from Web Features](https://md.research.elamur.ai/free/papers/bansal-2012-coreference-semantics-web-features/)
- [Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features](https://md.research.elamur.ai/free/papers/bjorkelund-2014-learning-structured-perceptrons-coreference-resolution/)
- [End-to-End Coreference Resolution via Hypergraph Partitioning](https://md.research.elamur.ai/free/papers/cai-2010-end-end-coreference-resolution-hypergraph/)
- [Evaluation Metrics For End-to-End Coreference Resolution Systems](https://md.research.elamur.ai/free/papers/cai-2010-evaluation-metrics-end-end-coreference/)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://md.research.elamur.ai/free/papers/cattan-2021-realistic-evaluation-principles-cross-document/)
- [Chinese Event Coreference Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers](https://md.research.elamur.ai/free/papers/chen-2015-chinese-event-coreference-resolution-unsupervised/)
- [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/papers/clark-2015-entity-centric-coreference-resolution-model/)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://md.research.elamur.ai/free/papers/clark-2016-deep-reinforcement-learning-mention-ranking/)
- [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/)
- [The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution](https://md.research.elamur.ai/free/papers/emami-2019-knowref-coreference-corpus-removing-gender/)
- [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/papers/fernandes-2014-latent-trees-coreference-resolution/)
- [Coreference Resolution in a Modular, Entity-Centered Model](https://md.research.elamur.ai/free/papers/haghighi-2010-coreference-resolution-modular-entity-centered/)
- [Narrative Schema as World Knowledge for Coreference Resolution](https://md.research.elamur.ai/free/papers/irwin-2011-narrative-schema-as-world-knowledge/)
- [Coreference Resolution with Entity Equalization](https://md.research.elamur.ai/free/papers/kantor-2019-coreference-resolution-entity-equalization/)
- [An Incremental Entity-Mention Model for Coreference Resolution with Restrictive Antecedent Accessibility](https://md.research.elamur.ai/free/papers/klenner-2011-incremental-entity-mention-model-coreference/)
- [Rule-based Coreference Resolution in German Historic Novels](https://md.research.elamur.ai/free/papers/krug-2015-rule-based-coreference-resolution-german/)
- [Singletons and Coreference Resolution Evaluation](https://md.research.elamur.ai/free/papers/kubler-2011-singletons-coreference-resolution-evaluation/)
- [Error-Driven Analysis of Challenges in Coreference Resolution](https://md.research.elamur.ai/free/papers/kummerfeld-2013-error-driven-analysis-challenges-coreference/)
- [Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking](https://md.research.elamur.ai/free/papers/kundu-2018-neural-cross-lingual-coreference-resolution/)
- [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/papers/lee-2013-deterministic-coreference-resolution-based-entity/)
- [Multigraph Clustering for Unsupervised Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2013-multigraph-clustering-unsupervised-coreference-resolution/)
- [Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation](https://md.research.elamur.ai/free/papers/nath-2024-okay-let-s-do-this/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [Cross-lingual Coreference Resolution of Pronouns](https://md.research.elamur.ai/free/papers/novak-2014-cross-lingual-coreference-resolution-pronouns/)
- [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/)
- [Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation](https://md.research.elamur.ai/free/papers/pradhan-2014-scoring-coreference-partitions-predicted-mentions/)
- [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/)
- [Limited memory incremental coreference resolution](https://md.research.elamur.ai/free/papers/webster-2014-limited-memory-incremental-coreference-resolution/)
- [A Discriminative Hierarchical Model for Fast Coreference at Large Scale](https://md.research.elamur.ai/free/papers/wick-2012-discriminative-hierarchical-model-fast-coreference/)
- [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/)
- [UBIU: A Language-Independent System for Coreference Resolution](https://md.research.elamur.ai/free/papers/zhekova-2010-ubiu-language-independent-system-coreference/)

## Cite

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

```bibtex
@inproceedings{rahman2009supervised,
  title = {{Supervised Models for Coreference Resolution}},
  author = {Rahman, Altaf and Ng, Vincent},
  booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing},
  year = {2009},
  doi = {10.3115/1699571.1699639},
  url = {https://doi.org/10.3115/1699571.1699639}
}
```

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## References

40 references, 17 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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