---
title: "Unsupervised Models for Coreference Resolution — Ng 2008"
description: "Supervised coreference resolvers depend heavily on annotated corpora, which makes them hard to transfer to languages with little labelled data."
authors: ["Vincent Ng"]
year: 2008
venue: "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D08-1067/"
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/ng-2008-unsupervised-models-coreference-resolution/"
---
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# Unsupervised Models for Coreference Resolution

**Authors:** [Vincent Ng](https://md.research.elamur.ai/free/authors/vincent-ng/) · **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/D08-1067/) · [PDF](https://aclanthology.org/D08-1067.pdf) · [OpenAlex](https://openalex.org/W2124700572)

**Citations:** 197 citations (openalex-snapshot · 2026-10-14) · [26](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Supervised coreference resolvers depend heavily on annotated corpora, which makes them hard to transfer to languages with little labelled data. Earlier attempts to bootstrap pairwise classifiers also drifted toward tagging nearly every pair as non-coreferent. Ng instead proposes a generative, document-level model that treats coreference as clustering learned with EM. Each document is represented through its mention pairs. Every pair is described by seven automatically computed features, split into three conditionally independent groups.

Enumerating all partitions is intractable, so the E-step keeps only the N most probable ones, found with Luo et al.'s (2004) Bell tree beam search. Learning is seeded with a single coreference-annotated document. The author also analyses Haghighi and Klein's (2007) Bayesian model and adds three fixes to it.

On ACE broadcast news and newswire data, scored with MUC and CEAF, the EM model clearly beats the original H&K model and roughly matches the modified one. It still trails a supervised resolver. The results suggest that constraint-aware unsupervised clustering is a workable path for low-resource coreference.

## Key points
- **Features:** the seven pair features fall into three groups.
  - Strong indicators: STR MATCH, ALIAS, APPOSITIVE.
  - Linguistic constraints: GENDER, NUMBER, SEM CLASS.
  - Mention type: NPTYPE.
  - The model's parameters are multinomials over each group, conditioned on whether the pair is coreferent, estimated with add-one smoothing.
- **Fixes to H&K:** the three modifications are relaxed head generation, agreement constraints and pronoun-only salience. Together they raise H&K's F-score by 5–8% (MUC) and 7–12% (CEAF), mainly through better precision.
- **Gain over original H&K:** the EM model outperforms it by 6–16% MUC F-score and 6–14% CEAF F-score.
- **True-mention results (MUC F):** the EM model scores 62.8 on BNEWS and 55.7 on NWIRE. The fully supervised model scores 61.1 and 59.6.
- **Supervised comparison system:** a C4.5 decision tree with the 34 features of Ng (2007), trained on the full ACE 2003 coreference training corpus and clustered with the Bell tree search.

*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

26 papers of our library cite it (of the 140673 whose bibliographies we read); 16 of them have a page here, 10 not yet.

- [Unsupervised Event Coreference Resolution with Rich Linguistic Features](https://md.research.elamur.ai/free/papers/bejan-2010-unsupervised-event-coreference-resolution-rich/)
- [Unsupervised Event Coreference Resolution](https://md.research.elamur.ai/free/papers/bejan-2014-unsupervised-event-coreference-resolution/)
- [End-to-End Coreference Resolution via Hypergraph Partitioning](https://md.research.elamur.ai/free/papers/cai-2010-end-end-coreference-resolution-hypergraph/)
- [Resolving Object and Attribute Coreference in Opinion Mining](https://md.research.elamur.ai/free/papers/ding-2010-resolving-object-attribute-coreference-opinion/)
- [The Same-Head Heuristic for Coreference](https://md.research.elamur.ai/free/papers/elsner-2010-same-head-heuristic-coreference/)
- [Bootstrapping coreference resolution using word associations](https://md.research.elamur.ai/free/papers/kobdani-2011-bootstrapping-coreference-resolution-word-associations/)
- [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/papers/lee-2013-deterministic-coreference-resolution-based-entity/)
- [A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models](https://md.research.elamur.ai/free/papers/li-2011-pronoun-anaphora-resolution-system-based/)
- [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2016-unsupervised-ranking-model-entity-coreference/)
- [Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/)
- [Multigraph Clustering for Unsupervised Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2013-multigraph-clustering-unsupervised-coreference-resolution/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [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 Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/)
- [The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors](https://md.research.elamur.ai/free/papers/vala-2016-more-antecedents-merrier-resolving-multi/)

## Cite

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

```bibtex
@inproceedings{ng2008unsupervised,
  title = {{Unsupervised Models for Coreference Resolution}},
  author = {Ng, Vincent},
  booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
  year = {2008},
  doi = {10.3115/1613715.1613795},
  url = {https://doi.org/10.3115/1613715.1613795}
}
```

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## References

23 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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