--- title: "Simple Coreference Resolution with Rich — Haghighi 2009" description: "The paper shows that a deliberately simple, deterministic coreference system can compete with much more complex learned models if it uses good syntactic…" authors: ["Aria Haghighi", "Dan Klein"] year: 2009 venue: "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D09-1120/" doi: "10.3115/1699648.1699661" 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/en/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2009](https://md.research.elamur.ai/free/en/papers/2009/) / Simple Coreference Resolution with Rich Syntactic and Semantic Features # Simple Coreference Resolution with Rich Syntactic and Semantic Features **Authors:** Aria Haghighi, [Dan Klein](https://md.research.elamur.ai/free/en/authors/dan-klein/) · **Year:** 2009 · **Venue:** Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP 2009) **Link:** [aclanthology.org/D09-1120](https://aclanthology.org/D09-1120/) (ACL Anthology D09-1120) **License of the paper:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — [creativecommons.org/licenses/by-nc-sa/3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) [**195** citations](https://openalex.org/works?filter=cites:W2139354869) OpenAlex · 2026-10-14 · [**82** citations in our library](https://md.research.elamur.ai/free/en/papers/haghighi-2009-simple-coreference-resolution-rich-syntactic/cited-by/) of the 140884 papers whose bibliographies we read · [Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D09-1/) · [licence](https://creativecommons.org/licenses/by-nc-sa/3.0/) - [Publisher's page](https://aclanthology.org/D09-1120/) - [PDF](https://aclanthology.org/D09-1120.pdf) - [DOI 10.3115/1699648.1699661](https://doi.org/10.3115/1699648.1699661) - [OpenAlex](https://openalex.org/W2139354869) **Authors (2)** 1. [Aria Haghighi](https://openalex.org/A5039365507) [ORCID](https://orcid.org/0000-0002-4997-0353) · [Semantic Scholar](https://www.semanticscholar.org/author/1761880) · h 22 · 2904 citations · 48 works 2. [Dan Klein](https://openalex.org/A5004921249) [ORCID](https://orcid.org/0000-0002-8881-1902) · [Semantic Scholar](https://www.semanticscholar.org/author/38666915) · h 74 · 25166 citations · 293 works Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart. ## Summary The paper shows that a deliberately simple, deterministic coreference system can compete with much more complex learned models if it uses good syntactic and semantic information. Each mention is linked to an antecedent chosen with a small set of modular components rather than a trained pairwise classifier. On the syntactic side, the system uses constraints read off the parse tree, such as appositives and predicate nominatives, and ranks candidate antecedents by their position in the tree instead of by flat surface distance. On the semantic side, it filters candidate pairs for compatibility using head-word relations mined in an unsupervised way from a large unlabeled corpus. The authors report that this simple system was competitive with, and in places better than, the state-of-the-art supervised and unsupervised systems of the time on standard coreference benchmarks. The paper was influential in showing that much of the work in coreference is done by syntax and lexical semantics rather than by elaborate learning. ## How Mova uses it - the development notes of `coref` and `coref/src/sieve.rs`: Mova's coreference module is a deterministic multi-pass sieve (architecture after Raghunathan et al. 2010 and Lee et al. 2013). From this paper it takes the principle that candidates higher in the parse tree come first. Candidate antecedents are sorted by the depth of their head (root = 0), then by position, in the Hobbs-style traversal order. - Tree depth is precomputed per sentence (`coref/src/doc.rs`), so the ordering is an integer sort, which keeps every linking decision explainable. ## Effectiveness in Mova One ordering principle inside the coreference sieve. Not measured separately; there is no ablation of the depth ordering on its own. --- 👨‍🔬💥 Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1699648.1699661](https://doi.org/10.3115/1699648.1699661) ```bibtex @inproceedings{haghighi2009simple, title = {{Simple Coreference Resolution with Rich Syntactic and Semantic Features}}, author = {Haghighi, Aria and Klein, Dan}, booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing}, year = {2009}, doi = {10.3115/1699648.1699661}, url = {https://doi.org/10.3115/1699648.1699661} } ``` - [← previous Sentence Boundary Detection and the Problem with the U.S.](https://md.research.elamur.ai/free/en/papers/aclbulk-2009-gillick-sentence-boundary-detection-problem-u/) - [next → Statistical Approaches to Computer-Assisted Translation](https://md.research.elamur.ai/free/en/papers/barrachina-2009-statistical-approaches-computer-assisted-translation/) - [↑ back to papers of 2009](https://md.research.elamur.ai/free/en/papers/2009/) ## References 10 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 MUC7. [find](https://scholar.google.com/scholar?q=B%2E+Amit+and+B%2E+Baldwin%2E+1998%2E+Algorithms+for+scoring+coreference+chains%2E+In+MUC7%2E) [our card](https://md.research.elamur.ai/free/en/papers/bagga-1998-algorithms-scoring-coreference-chains/) 2. Eric Bengston and Dan Roth. 2008. Understanding the value of features for corefernce resolution. In Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Eric+Bengston+and+Dan+Roth%2E+2008%2E+Understanding+the+value+of+features+for+corefernce+resolution%2E+In+Empirical+Methods+in+Natural+Language+Processing%2E) 3. E. Charniak. 2000. Maximum entropy inspired parser. In North American Chapter of the Association of Computational Linguistics (NAACL). [find](https://scholar.google.com/scholar?q=E%2E+Charniak%2E+2000%2E+Maximum+entropy+inspired+parser%2E+In+North+American+Chapter+of+the+Association+of+Computational+Linguistics+%28NAACL%29%2E) 4. Mike Collins. 1999. Head-driven statistical models for natural language parsing. [find](https://scholar.google.com/scholar?q=Mike+Collins%2E+1999%2E+Head%2Ddriven+statistical+models+for+natural+language+parsing%2E) [our card](https://md.research.elamur.ai/free/en/papers/cl-2003-collins-head-driven-statistical-models-natural/) 5. 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+Culotta%2C+M+Wick%2C+R+Hall%2C+and+A+McCallum%2E+2007%2E+First%2Dorder+probabilistic+models+for+coreference+resolution%2E+In+NAACL%2DHLT%2E) [our card](https://md.research.elamur.ai/free/en/papers/culotta-2007-first-order-probabilistic-models-coreference/) 6. Pascal Denis and Jason Baldridge. 2007. Global, Joint Determination of Anaphoricity and Coreference Resolution using Integer Programming. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=Pascal+Denis+and+Jason+Baldridge%2E+2007%2E+Global%2C+Joint+Determination+of+Anaphoricity+and+Coreference+Resolution+using+Integer+Programming%2E+In+HLT%2DNAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/denis-2007-joint-determination-anaphoricity-coreference-resolution/) 7. Jenny Finkel and Christopher Manning. 2008. Enforcing transitivity in coreference resolution. In Association of Computational Linguists (ACL). [find](https://scholar.google.com/scholar?q=Jenny+Finkel+and+Christopher+Manning%2E+2008%2E+Enforcing+transitivity+in+coreference+resolution%2E+In+Association+of+Computational+Linguists+%28ACL%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/finkel-2008-enforcing-transitivity-coreference-resolution/) 8. Jenny Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In ACL. [find](https://scholar.google.com/scholar?q=Jenny+Finkel%2C+Trond+Grenager%2C+and+Christopher+Manning%2E+2005%2E+Incorporating+non%2Dlocal+information+into+information+extraction+systems+by+gibbs+sampling%2E+In+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/finkel-2005-incorporating-non-local-information-into/) 9. Barbara J. Grosz, Aravind K. Joshi, and Scott Weinstein. 1995. Centering: A framework for modelling the local coherence of discourse. [find](https://scholar.google.com/scholar?q=Barbara+J%2E+Grosz%2C+Aravind+K%2E+Joshi%2C+and+Scott+Weinstein%2E+1995%2E+Centering%3A+A+framework+for+modelling+the+local+coherence+of+discourse%2E) 10. Aria Haghighi and Dan Klein. 2007. Unsupervised coreference resolution in a nonparametric bayesian model. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Association for Computational Linguistics. Comment 2 general appos. patterns 2 cataphora [find](https://scholar.google.com/scholar?q=Aria+Haghighi+and+Dan+Klein%2E+2007%2E+Unsupervised+coreference+resolution+in+a+nonparametric+bayesian+model%2E+In+Proceedings+of+the+45th+Annual+Meeting+of+the+Association+of+Computational+Linguistics%2E+Ass) [our card](https://md.research.elamur.ai/free/en/papers/haghighi-2007-unsupervised-coreference-resolution-nonparametric-bayesian/)