---
title: "Easy-first Coreference Resolution — Stoyanov 2012"
description: "Stoyanov and Eisner tackle within-document noun phrase coreference resolution."
authors: ["Veselin Stoyanov", "Jason Eisner"]
year: 2012
venue: "Proceedings of COLING 2012"
source: "https://aclanthology.org/C12-1154/"
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/stoyanov-2012-easy-first-coreference-resolution/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/stoyanov-2012-easy-first-coreference-resolution/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2012](https://md.research.elamur.ai/free/papers/2012/) / Easy-first Coreference Resolution

# Easy-first Coreference Resolution

**Authors:** [Veselin Stoyanov](https://md.research.elamur.ai/free/authors/veselin-stoyanov/), [Jason Eisner](https://md.research.elamur.ai/free/authors/jason-eisner/) · **Year:** 2012 · **Venue:** Proceedings of COLING 2012 · **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/C12-1154/) · [PDF](https://aclanthology.org/C12-1154.pdf) · [OpenAlex](https://openalex.org/W7096668150)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Stoyanov and Eisner tackle within-document noun phrase coreference resolution. They target a weakness of the dominant mention-pair approach: because it judges mentions only two at a time, it often yields incoherent clusters, such as chains that mix male and female referents. Their starting point is the multi-pass sieve system of Raghunathan et al. (2010), which links confident cases first. They keep that "easy-first" idea but replace hand-crafted rules with learned weights.

The system performs greedy agglomerative clustering. At each step it scores every possible merge of two existing clusters, plus a special stop action, with a linear model, then executes the best-scoring one. Features describe whole partial clusters, so earlier merges inform later, harder ones such as pronoun links. Training follows Goldberg and Elhadad's easy-first parser and uses perceptron-style updates during clustering, so the learner sees partial states like those met at test time.

On ACE data the method beats a strong pairwise baseline in most settings and also outperforms Raghunathan et al. This shows that learned greedy search can enforce global consistency cheaply.

## Key points
- Clustering is framed as an agent choosing between JOIN i j (merge two chains) and HALT. A priority queue holds the candidate actions, O(n²) of them for n clusters.
- Weights come from structured-perceptron-style training applied during greedy inference, so they are tuned to the actual test-time search procedure.
- The system beats a competitive pairwise-classifier baseline in 9 of 10 evaluation settings, which cover MUC and BCubed scores across corpora.
- It outperforms the sieve system of Raghunathan et al. (2010) by more than 2 points in all 4 directly comparable settings.
- The authors claim new state-of-the-art results with gold-standard mentions and for end-to-end systems on three ACE corpora. Qualitative examples from ACE04 and ACE05 show that the model avoids clusters with conflicting names or genders.

*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

25 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 12 not yet.

- [2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems](https://md.research.elamur.ai/free/papers/ahmed-2023-2-n-is-better-than/)
- [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/)
- [A Constrained Latent Variable Model for Coreference Resolution](https://md.research.elamur.ai/free/papers/chang-2013-constrained-latent-variable-model-coreference/)
- [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/papers/clark-2015-entity-centric-coreference-resolution-model/)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://md.research.elamur.ai/free/papers/clark-2016-improving-coreference-resolution-learning-entity/)
- [Decentralized Entity-Level Modeling for Coreference Resolution](https://md.research.elamur.ai/free/papers/durrett-2013-decentralized-entity-level-modeling-coreference/)
- [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/papers/lee-2013-deterministic-coreference-resolution-based-entity/)
- [Prune-and-Score: Learning for Greedy Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2014-prune-score-learning-greedy-coreference/)
- [Latent Structures for Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2015-latent-structures-coreference-resolution/)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://md.research.elamur.ai/free/papers/toshniwal-2020-learning-ignore-long-document-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/)
- [Dynamic Knowledge-Base Alignment for Coreference Resolution](https://md.research.elamur.ai/free/papers/zheng-2013-dynamic-knowledge-base-alignment-coreference/)

## Cite

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

```bibtex
@inproceedings{stoyanov2012easy,
  title = {{Easy-first Coreference Resolution}},
  author = {Stoyanov, Veselin and Eisner, Jason},
  booktitle = {Proceedings of COLING 2012},
  year = {2012},
  url = {https://aclanthology.org/C12-1154/}
}
```

- [← previous Domain-specific vs. Uniform Modeling for Coreference Resolution](https://md.research.elamur.ai/free/papers/uryupina-2012-domain-specific-vs-uniform-modeling/)
- [next → Evaluation of Discourse Relation Annotation in the Hindi Discourse Relation Bank](https://md.research.elamur.ai/free/papers/kolachina-2012-evaluation-discourse-relation-annotation-hindi/)
- [↑ back to papers of 2012](https://md.research.elamur.ai/free/papers/2012/)

## References

33 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Bagga, A. and Baldwin, B. (1998). Algorithms for scoring coreference chains. In Linguistic Coreference Workshop at LREC 1998. [find](https://scholar.google.com/scholar?q=Bagga%2C+A%2E+and+Baldwin%2C+B%2E+%281998%29%2E+Algorithms+for+scoring+coreference+chains%2E+In+Linguistic+Coreference+Workshop+at+LREC+1998%2E) [our card](https://md.research.elamur.ai/free/papers/bagga-1998-algorithms-scoring-coreference-chains/)
2. Bengtson, E. and Roth, D. (2008). Understanding the value of features for coreference resolution. In Proceedings of EMNLP, pages 294–303. [find](https://scholar.google.com/scholar?q=Bengtson%2C+E%2E+and+Roth%2C+D%2E+%282008%29%2E+Understanding+the+value+of+features+for+coreference+resolution%2E+In+Proceedings+of+EMNLP%2C+pages+294%E2%80%93303%2E) [our card](https://md.research.elamur.ai/free/papers/bengtson-2008-understanding-value-features-coreference-resolution/)
3. Bergsma, S. and Lin, D. (2006). Bootstrapping path-based pronoun resolution. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Bergsma%2C+S%2E+and+Lin%2C+D%2E+%282006%29%2E+Bootstrapping+path%2Dbased+pronoun+resolution%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/papers/bergsma-2006-bootstrapping-path-based-pronoun-resolution/)
4. Collins, M. and Roark, B. (2004). Incremental parsing with the perceptron algorithm. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=Collins%2C+M%2E+and+Roark%2C+B%2E+%282004%29%2E+Incremental+parsing+with+the+perceptron+algorithm%2E+In+Proceedings+of+ACL%2E) [our card](https://md.research.elamur.ai/free/papers/collins-2004-incremental-parsing-perceptron-algorithm/)
5. Culotta, A., Wick, M., Hall, R., and McCallum, A. (2007). First-order probabilistic models for coreference resolution. In Proceedings of HLT/NAACL, pages 81–88. [find](https://scholar.google.com/scholar?q=Culotta%2C+A%2E%2C+Wick%2C+M%2E%2C+Hall%2C+R%2E%2C+and+McCallum%2C+A%2E+%282007%29%2E+First%2Dorder+probabilistic+models+for+coreference+resolution%2E+In+Proceedings+of+HLT%2FNAACL%2C+pages+81%E2%80%9388%2E) [our card](https://md.research.elamur.ai/free/papers/culotta-2007-first-order-probabilistic-models-coreference/)
6. Daumé, H., Langford, J., and Marcu, D. (2009). Search-based structured prediction. Machine learning, 75(3):297–325. [find](https://scholar.google.com/scholar?q=Daum%C3%A9%2C+H%2E%2C+Langford%2C+J%2E%2C+and+Marcu%2C+D%2E+%282009%29%2E+Search%2Dbased+structured+prediction%2E+Machine+learning%2C+75%283%29%3A297%E2%80%93325%2E)
7. Daumé III, H. and Marcu, D. (2005). Learning as search optimization: Approximate large margin methods for structured prediction. In Proceedings of ICML, pages 169–176. [find](https://scholar.google.com/scholar?q=Daum%C3%A9+III%2C+H%2E+and+Marcu%2C+D%2E+%282005%29%2E+Learning+as+search+optimization%3A+Approximate+large+margin+methods+for+structured+prediction%2E+In+Proceedings+of+ICML%2C+pages+169%E2%80%93176%2E)
8. Freund, Y. and Schapire, R. (1999). Large margin classification using the perceptron algorithm. Machine learning, 37(3):277–296. [find](https://scholar.google.com/scholar?q=Freund%2C+Y%2E+and+Schapire%2C+R%2E+%281999%29%2E+Large+margin+classification+using+the+perceptron+algorithm%2E+Machine+learning%2C+37%283%29%3A277%E2%80%93296%2E)
9. Goldberg, Y. and Elhadad, M. (2010). An efficient algorithm for easy-first non-directional dependency parsing. In Proceedings of NAACL, pages 742–750. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Goldberg%2C+Y%2E+and+Elhadad%2C+M%2E+%282010%29%2E+An+efficient+algorithm+for+easy%2Dfirst+non%2Ddirectional+dependency+parsing%2E+In+Proceedings+of+NAACL%2C+pages+742%E2%80%93750%2E+Association+for+Computational+Linguistics%2E)
10. Haghighi, A. and Klein, D. (2010). Coreference resolution in a modular, entity-centered model. In Proceedings of NAACL, pages 385–393. [find](https://scholar.google.com/scholar?q=Haghighi%2C+A%2E+and+Klein%2C+D%2E+%282010%29%2E+Coreference+resolution+in+a+modular%2C+entity%2Dcentered+model%2E+In+Proceedings+of+NAACL%2C+pages+385%E2%80%93393%2E) [our card](https://md.research.elamur.ai/free/papers/haghighi-2010-coreference-resolution-modular-entity-centered/)
11. Hasler, L., Orăsan, C., and Naumann, K. (2006). NPs for events: Experiments in coreference annotation. In Proceedings of LREC2006. [find](https://scholar.google.com/scholar?q=Hasler%2C+L%2E%2C+Ora%CC%86san%2C+C%2E%2C+and+Naumann%2C+K%2E+%282006%29%2E+NPs+for+events%3A+Experiments+in+coreference+annotation%2E+In+Proceedings+of+LREC2006%2E) [our card](https://md.research.elamur.ai/free/papers/hasler-2006-nps-events-experiments-coreference-annotation/)
12. Huang, L., Fayong, S., and Guo, Y. (2012). Structured perceptron with inexact search. In Proceedings of NAACL. [find](https://scholar.google.com/scholar?q=Huang%2C+L%2E%2C+Fayong%2C+S%2E%2C+and+Guo%2C+Y%2E+%282012%29%2E+Structured+perceptron+with+inexact+search%2E+In+Proceedings+of+NAACL%2E) [our card](https://md.research.elamur.ai/free/papers/huang-2012-structured-perceptron-inexact-search/)
13. Jain, V., Turaga, S., Briggman, K., Helmstaedter, M., Denk, W., and Seung, H. (2011). Learning to agglomerate superpixel hierarchies. In Proceedings of NIPS. [find](https://scholar.google.com/scholar?q=Jain%2C+V%2E%2C+Turaga%2C+S%2E%2C+Briggman%2C+K%2E%2C+Helmstaedter%2C+M%2E%2C+Denk%2C+W%2E%2C+and+Seung%2C+H%2E+%282011%29%2E+Learning+to+agglomerate+superpixel+hierarchies%2E+In+Proceedings+of+NIPS%2E)
14. Kruskal, J. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society, 7(1):48–50. [find](https://scholar.google.com/scholar?q=Kruskal%2C+J%2E+%281956%29%2E+On+the+shortest+spanning+subtree+of+a+graph+and+the+traveling+salesman+problem%2E+Proceedings+of+the+American+Mathematical+society%2C+7%281%29%3A48%E2%80%9350%2E)
15. MUC-6 (1995). Coreference task definition. In Proceedings of the Sixth Message Understanding Conference (MUC-6), pages 335–344. [find](https://scholar.google.com/scholar?q=MUC%2D6+%281995%29%2E+Coreference+task+definition%2E+In+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29%2C+pages+335%E2%80%93344%2E)
16. MUC-7 (1997). Coreference task definition. In Proceedings of the Seventh Message Understanding Conference (MUC-7). [find](https://scholar.google.com/scholar?q=MUC%2D7+%281997%29%2E+Coreference+task+definition%2E+In+Proceedings+of+the+Seventh+Message+Understanding+Conference+%28MUC%2D7%29%2E)
17. Ng, V. and Cardie, C. (2002). Improving machine learning approaches to coreference resolution. In Proceedings of ACL, pages 104–111. [find](https://scholar.google.com/scholar?q=Ng%2C+V%2E+and+Cardie%2C+C%2E+%282002%29%2E+Improving+machine+learning+approaches+to+coreference+resolution%2E+In+Proceedings+of+ACL%2C+pages+104%E2%80%93111%2E) [our card](https://md.research.elamur.ai/free/papers/ng-2002-improving-machine-learning-approaches-coreference/)
18. NIST (2004). The ACE Evaluation Plan. National Institute of Standards and Technology, United States of America. [find](https://scholar.google.com/scholar?q=NIST+%282004%29%2E+The+ACE+Evaluation+Plan%2E+National+Institute+of+Standards+and+Technology%2C+United+States+of+America%2E)
19. Poon, H. and Domingos, P. (2008). Joint unsupervised coreference resolution with Markov logic. In Proceedings of EMNLP, pages 650–659. [find](https://scholar.google.com/scholar?q=Poon%2C+H%2E+and+Domingos%2C+P%2E+%282008%29%2E+Joint+unsupervised+coreference+resolution+with+Markov+logic%2E+In+Proceedings+of+EMNLP%2C+pages+650%E2%80%93659%2E) [our card](https://md.research.elamur.ai/free/papers/poon-2008-joint-unsupervised-coreference-resolution-markov/)
20. Pradhan, S., Ramshaw, L., Marcus, M., Palmer, M., Weischedel, R., and Xue, N. (2011). Conll-2011 shared task: Modeling unrestricted coreference in ontonotes. In Proceedings of CoNLL 2011, pages 1–27. [find](https://scholar.google.com/scholar?q=Pradhan%2C+S%2E%2C+Ramshaw%2C+L%2E%2C+Marcus%2C+M%2E%2C+Palmer%2C+M%2E%2C+Weischedel%2C+R%2E%2C+and+Xue%2C+N%2E+%282011%29%2E+Conll%2D2011+shared+task%3A+Modeling+unrestricted+coreference+in+ontonotes%2E+In+Proceedings+of+CoNLL+2011%2C+pages+1%E2%80%9327%2E) [our card](https://md.research.elamur.ai/free/papers/pradhan-2011-conll-2011-shared-task-modeling/)
21. Raghunathan, K., Lee, H., Rangarajan, S., Chambers, N., Surdeanu, M., Jurafsky, D., and Manning, C. (2010). A multi-pass sieve for coreference resolution. In Proceedings of EMNLP, pages 492–501. [find](https://scholar.google.com/scholar?q=Raghunathan%2C+K%2E%2C+Lee%2C+H%2E%2C+Rangarajan%2C+S%2E%2C+Chambers%2C+N%2E%2C+Surdeanu%2C+M%2E%2C+Jurafsky%2C+D%2E%2C+and+Manning%2C+C%2E+%282010%29%2E+A+multi%2Dpass+sieve+for+coreference+resolution%2E+In+Proceedings+of+EMNLP%2C+pages+492%E2%80%93501%2E) [our card](https://md.research.elamur.ai/free/papers/raghunathan-2010-multi-pass-sieve-coreference-resolution/)
22. Ross, S., Gordon, G. J., and Bagnell, J. A. (2011). A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of AISTATS. [find](https://scholar.google.com/scholar?q=Ross%2C+S%2E%2C+Gordon%2C+G%2E+J%2E%2C+and+Bagnell%2C+J%2E+A%2E+%282011%29%2E+A+reduction+of+imitation+learning+and+structured+prediction+to+no%2Dregret+online+learning%2E+In+Proceedings+of+AISTATS%2E)
23. Soon, W., Ng, H., and Lim, D. (2001). A machine learning approach to coreference resolution of noun phrases. Computational linguistics, 27(4):521–544. [find](https://scholar.google.com/scholar?q=Soon%2C+W%2E%2C+Ng%2C+H%2E%2C+and+Lim%2C+D%2E+%282001%29%2E+A+machine+learning+approach+to+coreference+resolution+of+noun+phrases%2E+Computational+linguistics%2C+27%284%29%3A521%E2%80%93544%2E) [our card](https://md.research.elamur.ai/free/papers/soon-2001-machine-learning-approach-coreference-resolution/)
24. Stoyanov, V., Cardie, C., Gilbert, N., Riloff, E., Buttler, D., and Hysom, D. (2010a). Coreference resolution with Reconcile. In Proceedings of the ACL Short Papers, pages 156–161. [find](https://scholar.google.com/scholar?q=Stoyanov%2C+V%2E%2C+Cardie%2C+C%2E%2C+Gilbert%2C+N%2E%2C+Riloff%2C+E%2E%2C+Buttler%2C+D%2E%2C+and+Hysom%2C+D%2E+%282010a%29%2E+Coreference+resolution+with+Reconcile%2E+In+Proceedings+of+the+ACL+Short+Papers%2C+pages+156%E2%80%93161%2E) [our card](https://md.research.elamur.ai/free/papers/stoyanov-2010-coreference-resolution-reconcile/)
25. Stoyanov, V., Cardie, C., Gilbert, N., Riloff, E., Buttler, D., and Hysom, D. (2010b). Reconcile: A coreference resolution research platform. Technical report, Cornell University. [find](https://scholar.google.com/scholar?q=Stoyanov%2C+V%2E%2C+Cardie%2C+C%2E%2C+Gilbert%2C+N%2E%2C+Riloff%2C+E%2E%2C+Buttler%2C+D%2E%2C+and+Hysom%2C+D%2E+%282010b%29%2E+Reconcile%3A+A+coreference+resolution+research+platform%2E+Technical+report%2C+Cornell+University%2E)
26. Stoyanov, V. and Eisner, J. (2012). Minimum-risk training of approximate CRF-based NLP systems. In Proceedings of NAACL. [find](https://scholar.google.com/scholar?q=Stoyanov%2C+V%2E+and+Eisner%2C+J%2E+%282012%29%2E+Minimum%2Drisk+training+of+approximate+CRF%2Dbased+NLP+systems%2E+In+Proceedings+of+NAACL%2E)
27. Stoyanov, V., Gilbert, N., Cardie, C., and Riloff, E. (2009). Conundrums in noun phrase coreference resolution: Making sense of the state-of-the-art. In Proceedings of ACL/IJCNLP, pages 656–664. [find](https://scholar.google.com/scholar?q=Stoyanov%2C+V%2E%2C+Gilbert%2C+N%2E%2C+Cardie%2C+C%2E%2C+and+Riloff%2C+E%2E+%282009%29%2E+Conundrums+in+noun+phrase+coreference+resolution%3A+Making+sense+of+the+state%2Dof%2Dthe%2Dart%2E+In+Proceedings+of+ACL%2FIJCNLP%2C+pages+656%E2%80%93664%2E) [our card](https://md.research.elamur.ai/free/papers/stoyanov-2009-conundrums-noun-phrase-coreference-resolution/)
28. Stoyanov, V., Ropson, A., and Eisner, J. (2011). Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure. In Proceedings of AISTATS. [find](https://scholar.google.com/scholar?q=Stoyanov%2C+V%2E%2C+Ropson%2C+A%2E%2C+and+Eisner%2C+J%2E+%282011%29%2E+Empirical+risk+minimization+of+graphical+model+parameters+given+approximate+inference%2C+decoding%2C+and+model+structure%2E+In+Proceedings+of+AISTATS%2E)
29. Sutton, R., McAllester, D., Singh, S., and Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. NIPS. [find](https://scholar.google.com/scholar?q=Sutton%2C+R%2E%2C+McAllester%2C+D%2E%2C+Singh%2C+S%2E%2C+and+Mansour%2C+Y%2E+%282000%29%2E+Policy+gradient+methods+for+reinforcement+learning+with+function+approximation%2E+NIPS%2E)
30. Vilain, M., Burger, J., Aberdeen, J., Connolly, D., and Hirschman, L. (1995). A model-theoretic coreference scoring theme. In Proceedings of the Sixth Message Understanding Conference (MUC-6). [find](https://scholar.google.com/scholar?q=Vilain%2C+M%2E%2C+Burger%2C+J%2E%2C+Aberdeen%2C+J%2E%2C+Connolly%2C+D%2E%2C+and+Hirschman%2C+L%2E+%281995%29%2E+A+model%2Dtheoretic+coreference+scoring+theme%2E+In+Proceedings+of+the+Sixth+Message+Understanding+Conference+%28MUC%2D6%29%2E)
31. Watkins, C. and Dayan, P. (1992). Q-learning. Machine Learning, 8(3):279–292. [find](https://scholar.google.com/scholar?q=Watkins%2C+C%2E+and+Dayan%2C+P%2E+%281992%29%2E+Q%2Dlearning%2E+Machine+Learning%2C+8%283%29%3A279%E2%80%93292%2E)
32. Yang, X., Zhou, G., Su, J., and Tan, C. (2003). Coreference resolution using competition learning approach. In Proceedings of ACL, pages 176–183. [find](https://scholar.google.com/scholar?q=Yang%2C+X%2E%2C+Zhou%2C+G%2E%2C+Su%2C+J%2E%2C+and+Tan%2C+C%2E+%282003%29%2E+Coreference+resolution+using+competition+learning+approach%2E+In+Proceedings+of+ACL%2C+pages+176%E2%80%93183%2E) [our card](https://md.research.elamur.ai/free/papers/yang-2003-coreference-resolution-competition-learning-approach/)
33. Zhou, G. and Su, J. (2004). A high-performance coreference resolution system using a constraint-based multi-agent strategy. In Proceedings of COLING. [find](https://scholar.google.com/scholar?q=Zhou%2C+G%2E+and+Su%2C+J%2E+%282004%29%2E+A+high%2Dperformance+coreference+resolution+system+using+a+constraint%2Dbased+multi%2Dagent+strategy%2E+In+Proceedings+of+COLING%2E) [our card](https://md.research.elamur.ai/free/papers/zhou-2004-high-performance-coreference-resolution-system/)