[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/raghunathan-2010-multi-pass-sieve-coreference-resolution/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / A Multi-Pass Sieve for Coreference Resolution

# A Multi-Pass Sieve for Coreference Resolution

**Authors:** Karthik Raghunathan, [Heeyoung Lee](https://md.mova.elamur.ai/free/en/docs/papers/authors/heeyoung-lee/), Sudarshan Rangarajan, [Nathanael Chambers](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathanael-chambers/), [Mihai Surdeanu](https://md.mova.elamur.ai/free/en/docs/papers/authors/mihai-surdeanu/), [Dan Jurafsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-jurafsky/), [Christopher Manning](https://md.mova.elamur.ai/free/en/docs/papers/authors/christopher-manning/) · **Year:** 2010 · **Venue:** Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP) **Link:** [aclanthology.org/D10-1048](https://aclanthology.org/D10-1048/) (ACL Anthology D10-1048) **License of the paper:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — [creativecommons.org/licenses/by-nc-sa/3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/)

[**128** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/raghunathan-2010-multi-pass-sieve-coreference-resolution/) of the 140673 papers whose bibliographies we read[Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D10-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/D10-1048/)
- [PDF](https://aclanthology.org/D10-1048.pdf)

**Authors (7)**

1. Karthik Raghunathan
2. Heeyoung Lee
3. Sudarshan Rangarajan
4. Nathanael Chambers
5. Mihai Surdeanu
6. Dan Jurafsky
7. Christopher Manning

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## Summary

The paper proposes an unsupervised, deterministic coreference system built as a sequence of "sieves": independent rule-based passes applied from the most precise to the least precise. Each pass sees the clusters built by earlier passes, so later, weaker rules can use attributes shared across a whole entity cluster rather than a single mention. Early passes handle high-confidence cases such as exact string match and appositive or predicate-nominative constructions; later passes relax head matching and finally resolve pronouns under agreement constraints. Candidate antecedents are ordered by a syntactic tree traversal in the spirit of Hobbs. The authors report that this simple, modular design was competitive with or better than learned systems of the time on standard benchmarks; it became the basis of the Stanford deterministic coreference system.

## How Mova uses it

- `coref/src/sieve.rs` — the overall architecture: an ordered list of sieves from most to least precise (speaker, exact, relaxed, appos, pred, relpron, acronym, reflexive, strict head-match variants, proper_head, pronoun), each attaching only the first mention of its cluster and skipping indefinite mentions except on exact match.
- Candidate ordering in the Hobbs style, taken from the description in this paper and its follow-up (Lee et al. 2013): breadth-first left-to-right over the tree, own clause first, then the rest of the sentence, then previous sentences (left-to-right for pronouns, right-to-left for nominals).
- Cluster-level features: a cluster's number, gender, animacy and person are the union of its mentions' features, so pronoun agreement is checked against the whole entity.
- Every link carries the id of the sieve that made it, which `coref explain` prints as the reason.

## Effectiveness in Mova

Measured on GUM test (UD 2.18), head match, CoNLL F1 with / without singletons, gold trees: the default sieve (12 passes) scores 73.04 / 60.31, versus exact string match only 53.07 / 27.88 and "pronoun → nearest agreeing mention" 57.73 / 32.67; on Mova's own predicted trees 67.90 / 54.03. On dev, Hobbs-style candidate ordering beats pure recency: pronoun precision 64.2% vs 55.4%, CoNLL 73.43 / 63.34 vs 72.23 / 61.33. Two relaxed sieves were disabled because their precision on dev was too low (strict_c 31%, relaxed_head 24%).

---

👨‍🔬💥

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/)

## Cite

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

```
@inproceedings{raghunathan2010multi,
  title = {{A Multi-Pass Sieve for Coreference Resolution}},
  author = {Raghunathan, Karthik and Lee, Heeyoung and Rangarajan, Sudarshan and Chambers, Nathanael and Surdeanu, Mihai and Jurafsky, Dan and Manning, Christopher},
  booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing},
  year = {2010},
  url = {https://aclanthology.org/D10-1048/}
}
```

- [← previous C4.5: Programs for Machine Learning](https://md.mova.elamur.ai/free/en/docs/papers/quinlan-1993-c45-programs-machine-learning/)
- [next → Higher Lessons in English (1877/1886) and Graded Lessons in English (1877)](https://md.mova.elamur.ai/free/en/docs/papers/reed-1877-higher-lessons-english/)
- [↑ back to papers](https://md.mova.elamur.ai/free/en/docs/papers/)

## References

24 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. B. Amit and B. Baldwin. 1998. Algorithms for scoring coreference chains. In MUC-7. [find](https://scholar.google.com/scholar?q=B%2E+Amit+and+B%2E+Baldwin%2E+1998%2E+Algorithms+for+scoring+coreference+chains%2E+In+MUC%2D7%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bagga-1998-algorithms-scoring-coreference-chains/)
2. E. Bengston and D. Roth. 2008. Understanding the value of features for coreference resolution. In EMNLP. [find](https://scholar.google.com/scholar?q=E%2E+Bengston+and+D%2E+Roth%2E+2008%2E+Understanding+the+value+of+features+for+coreference+resolution%2E+In+EMNLP%2E)
3. S. Bergsma and D. Lin. 2006. Bootstrapping Path-Based Pronoun Resolution. In ACL-COLING. [find](https://scholar.google.com/scholar?q=S%2E+Bergsma+and+D%2E+Lin%2E+2006%2E+Bootstrapping+Path%2DBased+Pronoun+Resolution%2E+In+ACL%2DCOLING%2E)
4. P.F. Brown, V.J. Della Pietra, S.A. Della Pietra, and R.L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19. [find](https://scholar.google.com/scholar?q=P%2EF%2E+Brown%2C+V%2EJ%2E+Della+Pietra%2C+S%2EA%2E+Della+Pietra%2C+and+R%2EL%2E+Mercer%2E+1993%2E+The+mathematics+of+statistical+machine+translation%3A+Parameter+estimation%2E+Computational+Linguistics%2C+19%2E)
5. M. Collins and Y. Singer. 1999. Unsupervised models for named entity classification. In EMNLP-VLC. [find](https://scholar.google.com/scholar?q=M%2E+Collins+and+Y%2E+Singer%2E+1999%2E+Unsupervised+models+for+named+entity+classification%2E+In+EMNLP%2DVLC%2E)
6. A. Culotta, M. Wick, R. Hall, and A. McCallum. 2007. First-order probabilistic models for coreference resolution. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=A%2E+Culotta%2C+M%2E+Wick%2C+R%2E+Hall%2C+and+A%2E+McCallum%2E+2007%2E+First%2Dorder+probabilistic+models+for+coreference+resolution%2E+In+NAACL%2DHLT%2E)
7. M. Elsner and E. Charniak. 2010. The same-head heuristic for coreference. In ACL. [find](https://scholar.google.com/scholar?q=M%2E+Elsner+and+E%2E+Charniak%2E+2010%2E+The+same%2Dhead+heuristic+for+coreference%2E+In+ACL%2E)
8. J. Finkel, T. Grenager, and C. Manning. 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. In ACL. [find](https://scholar.google.com/scholar?q=J%2E+Finkel%2C+T%2E+Grenager%2C+and+C%2E+Manning%2E+2005%2E+Incorporating+non%2Dlocal+information+into+information+extraction+systems+by+Gibbs+sampling%2E+In+ACL%2E)
9. J. Finkel and C. Manning. 2008. Enforcing transitivity in coreference resolution. In ACL. [find](https://scholar.google.com/scholar?q=J%2E+Finkel+and+C%2E+Manning%2E+2008%2E+Enforcing+transitivity+in+coreference+resolution%2E+In+ACL%2E)
10. B. A. Fox 1993. Discourse structure and anaphora: written and conversational English. Cambridge University Press. [find](https://scholar.google.com/scholar?q=B%2E+A%2E+Fox+1993%2E+Discourse+structure+and+anaphora%3A+written+and+conversational+English%2E+Cambridge+University+Press%2E)
11. J. Ghosh. 2003. Scalable clustering methods for data mining. Handbook of Data Mining, chapter 10, pages 247–277. [find](https://scholar.google.com/scholar?q=J%2E+Ghosh%2E+2003%2E+Scalable+clustering+methods+for+data+mining%2E+Handbook+of+Data+Mining%2C+chapter+10%2C+pages+247%E2%80%93277%2E)
12. A. Haghighi and D. Klein. 2009. Simple coreference resolution with rich syntactic and semantic features. In EMNLP. [find](https://scholar.google.com/scholar?q=A%2E+Haghighi+and+D%2E+Klein%2E+2009%2E+Simple+coreference+resolution+with+rich+syntactic+and+semantic+features%2E+In+EMNLP%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/)
13. A. Haghighi and D. Klein. 2010. Coreference resolution in a modular, entity-centered model. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=A%2E+Haghighi+and+D%2E+Klein%2E+2010%2E+Coreference+resolution+in+a+modular%2C+entity%2Dcentered+model%2E+In+HLT%2DNAACL%2E)
14. J.R. Hobbs. 1977. Resolving pronoun references. Lingua. [find](https://scholar.google.com/scholar?q=J%2ER%2E+Hobbs%2E+1977%2E+Resolving+pronoun+references%2E+Lingua%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/hobbs-1978-resolving-pronoun-references/)
15. H. Ji and D. Lin. 2009. Gender and animacy knowledge discovery from web-scale n-grams for unsupervised person mention detection. In PACLIC. [find](https://scholar.google.com/scholar?q=H%2E+Ji+and+D%2E+Lin%2E+2009%2E+Gender+and+animacy+knowledge+discovery+from+web%2Dscale+n%2Dgrams+for+unsupervised+person+mention+detection%2E+In+PACLIC%2E)
16. L. Kertz, A. Kehler, and J. Elman. 2006. Grammatical and Coherence-Based Factors in Pronoun Interpretation. In Proceedings of the 28th Annual Conference of the Cognitive Science Society. [find](https://scholar.google.com/scholar?q=L%2E+Kertz%2C+A%2E+Kehler%2C+and+J%2E+Elman%2E+2006%2E+Grammatical+and+Coherence%2DBased+Factors+in+Pronoun+Interpretation%2E+In+Proceedings+of+the+28th+Annual+Conference+of+the+Cognitive+Science+Society%2E)
17. D. Klein and C. Manning. 2003. Accurate unlexicalized parsing. In ACL. [find](https://scholar.google.com/scholar?q=D%2E+Klein+and+C%2E+Manning%2E+2003%2E+Accurate+unlexicalized+parsing%2E+In+ACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2003-klein-accurate-unlexicalized-parsing/)
18. X. Luo. 2005. On coreference resolution performance metrics. In HTL-EMNLP. [find](https://scholar.google.com/scholar?q=X%2E+Luo%2E+2005%2E+On+coreference+resolution+performance+metrics%2E+In+HTL%2DEMNLP%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/luo-2005-coreference-resolution-performance-metrics/)
19. H. Poon and P. Domingos. 2008. Joint unsupervised coreference resolution with Markov Logic. In EMNLP. [find](https://scholar.google.com/scholar?q=H%2E+Poon+and+P%2E+Domingos%2E+2008%2E+Joint+unsupervised+coreference+resolution+with+Markov+Logic%2E+In+EMNLP%2E)
20. A.S. Schwartz and M.A. Hearst. 2003. A simple algorithm for identifying abbrevation definitions in biomedical text. In Pacific Symposium on Biocomputing. [find](https://scholar.google.com/scholar?q=A%2ES%2E+Schwartz+and+M%2EA%2E+Hearst%2E+2003%2E+A+simple+algorithm+for+identifying+abbrevation+definitions+in+biomedical+text%2E+In+Pacific+Symposium+on+Biocomputing%2E)
21. B.F. Skinner. 1938. The behavior of organisms: An experimental analysis. Appleton-Century-Crofts. [find](https://scholar.google.com/scholar?q=B%2EF%2E+Skinner%2E+1938%2E+The+behavior+of+organisms%3A+An+experimental+analysis%2E+Appleton%2DCentury%2DCrofts%2E)
22. V.I. Spitkovsky, H. Alshawi, and D. Jurafsky. 2010. From baby steps to leapfrog: How “less is more” in unsupervised dependency parsing. In NAACL. [find](https://scholar.google.com/scholar?q=V%2EI%2E+Spitkovsky%2C+H%2E+Alshawi%2C+and+D%2E+Jurafsky%2E+2010%2E+From+baby+steps+to+leapfrog%3A+How+%E2%80%9Cless+is+more%E2%80%9D+in+unsupervised+dependency+parsing%2E+In+NAACL%2E)
23. V. Stoyanov, N. Gilbert, C. Cardie, and E. Riloff. 2010. Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art. In ACL-IJCNLP. [find](https://scholar.google.com/scholar?q=V%2E+Stoyanov%2C+N%2E+Gilbert%2C+C%2E+Cardie%2C+and+E%2E+Riloff%2E+2010%2E+Conundrums+in+noun+phrase+coreference+resolution%3A+making+sense+of+the+state%2Dof%2Dthe%2Dart%2E+In+ACL%2DIJCNLP%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/stoyanov-2009-conundrums-noun-phrase-coreference-resolution/)
24. M. Vilain, J. Burger, J. Aberdeen, D. Connolly, and L. Hirschman. 1995. A model-theoretic coreference scoring scheme. In MUC-6. [find](https://scholar.google.com/scholar?q=M%2E+Vilain%2C+J%2E+Burger%2C+J%2E+Aberdeen%2C+D%2E+Connolly%2C+and+L%2E+Hirschman%2E+1995%2E+A+model%2Dtheoretic+coreference+scoring+scheme%2E+In+MUC%2D6%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/vilain-1995-model-theoretic-coreference-scoring-scheme/)