--- title: "Joint Unsupervised Coreference Resolution with — Poon 2008" description: "Poon and Domingos address coreference resolution without labeled data." authors: ["Hoifung Poon", "Pedro Domingos"] year: 2008 venue: "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D08-1068/" 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/poon-2008-joint-unsupervised-coreference-resolution-markov/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/poon-2008-joint-unsupervised-coreference-resolution-markov/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2008](https://md.research.elamur.ai/free/papers/2008/) / Joint Unsupervised Coreference Resolution with Markov Logic # Joint Unsupervised Coreference Resolution with Markov Logic **Authors:** [Hoifung Poon](https://md.research.elamur.ai/free/authors/hoifung-poon/), Pedro Domingos · **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/D08-1068/) · [PDF](https://aclanthology.org/D08-1068.pdf) · [OpenAlex](https://openalex.org/W1992967856) **Citations:** 204 citations (openalex-snapshot · 2026-10-14) · [68](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Poon and Domingos address coreference resolution without labeled data. Supervised systems need costly annotated mention pairs, and earlier unsupervised models, notably Haghighi & Klein (2007), were clearly less accurate. The authors build a cluster-based model in Markov logic that reasons jointly over all mentions and enforces transitivity implicitly. Anaphoricity is folded into the same process instead of being decided beforehand. Because the model is expressed as weighted first-order formulas, linguistic cues such as apposition and predicate nominals can be added easily. Those cues create large dependent groups of mentions, which Gibbs-sampling approaches cannot handle well. For learning, the authors extend existing Markov logic algorithms to hidden predicates, producing the first general-purpose unsupervised learner for Markov logic. Inference uses Lazy-MC-SAT. On MUC-6 and ACE data, the system beats Haghighi & Klein even with very little training text, and it often equals or surpasses strong supervised systems. This shows that joint inference can largely make up for missing labels. ## Key points - Learning combines a gradient-based method (preconditioned scaled conjugate gradient, building on Lowd & Domingos) with samples from Lazy-MC-SAT, and the approach is built on the open-source Alchemy package. - On MUC-6, the full model MLN-HAN (better heads, apposition, predicate nominals) scores about 9% above Haghighi & Klein (2007) and about 6% above the supervised McCallum & Wellner (2005). Its B³ results are 77.4 precision, 67.6 recall and 72.2 F1. - Even when trained on single documents, the system's F1 is more than 7% higher than Haghighi & Klein's model trained on 60 documents. It also beats that model trained on 381 documents. - On ACE-2004, MLN-HAN reaches a B³ F1 of 70.0 (BNEWS) and 72.3 (NWIRE). On ACE-2 it clearly beats the supervised Ng (2005), ties Denis & Baldridge (2007) on NWIRE, and trails it somewhat on BNEWS and NPAPER. - To show the gains are not an artifact of merging all mentions, the authors also report pairwise precision and the error in cluster counts. Remaining errors mostly involve nominals and proper nouns with differing heads, such as "Mr. Bush" vs. "the White House". *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 68 papers of our library cite it (of the 140673 whose bibliographies we read); 25 of them have a page here, 43 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/) - [Poly-co: a multilayer perceptron approach for coreference detection](https://md.research.elamur.ai/free/papers/charton-2011-poly-co-multilayer-perceptron-approach/) - [A Unified Event Coreference Resolution by Integrating Multiple Resolvers](https://md.research.elamur.ai/free/papers/chen-2011-unified-event-coreference-resolution-integrating/) - [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/) - [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/) - [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/papers/fernandes-2014-latent-trees-coreference-resolution/) - [Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves](https://md.research.elamur.ai/free/papers/hajishirzi-2013-joint-coreference-resolution-named-entity/) - [Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction](https://md.research.elamur.ai/free/papers/hardmeier-2013-latent-anaphora-resolution-cross-lingual/) - [NARC – Norwegian Anaphora Resolution Corpus](https://md.research.elamur.ai/free/papers/hlum-2022-narc-norwegian-anaphora-resolution-corpus/) - [Global Inference for Bridging Anaphora Resolution](https://md.research.elamur.ai/free/papers/hou-2013-global-inference-bridging-anaphora-resolution/) - [Bootstrapping coreference resolution using word associations](https://md.research.elamur.ai/free/papers/kobdani-2011-bootstrapping-coreference-resolution-word-associations/) - [Joint Entity and Event Coreference Resolution across Documents](https://md.research.elamur.ai/free/papers/lee-2012-joint-entity-event-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/) - [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2016-unsupervised-ranking-model-entity-coreference/) - [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/) - [A Multi-Pass Sieve for Coreference Resolution](https://md.research.elamur.ai/free/papers/raghunathan-2010-multi-pass-sieve-coreference-resolution/) - [Translation-Based Projection for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2012-translation-based-projection-multilingual-coreference/) - [Coreference Resolution across Corpora: Languages, Coding Schemes, and Preprocessing Information](https://md.research.elamur.ai/free/papers/recasens-2010-coreference-resolution-across-corpora-languages/) - [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/) - [Easy-first Coreference Resolution](https://md.research.elamur.ai/free/papers/stoyanov-2012-easy-first-coreference-resolution/) - [Learning Global Features for Coreference Resolution](https://md.research.elamur.ai/free/papers/wiseman-2016-learning-global-features-coreference-resolution/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{poon2008joint, title = {{Joint Unsupervised Coreference Resolution with Markov Logic}}, author = {Poon, Hoifung and Domingos, Pedro}, booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing}, year = {2008}, doi = {10.3115/1613715.1613796}, url = {https://doi.org/10.3115/1613715.1613796} } ``` - [← previous Identifying Semitic Roots: Machine Learning with Linguistic Constraints](https://md.research.elamur.ai/free/papers/cl-2008-daya-identifying-semitic-roots-machine-learning/) - [next → Late Biblical Hebrew and the Qumran Pesher Habakkuk](https://md.research.elamur.ai/free/papers/hbo-2008-young-late-biblical-hebrew-qumran-pesher/) - [↑ back to papers of 2008](https://md.research.elamur.ai/free/papers/2008/) ## References 19 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Amit, B. & Baldwin, B. 1998. Algorithms for scoring coreference chains. In Proc. MUC-7. [find](https://scholar.google.com/scholar?q=Amit%2C+B%2E+%26+Baldwin%2C+B%2E+1998%2E+Algorithms+for+scoring+coreference+chains%2E+In+Proc%2E+MUC%2D7%2E) [our card](https://md.research.elamur.ai/free/papers/bagga-1998-algorithms-scoring-coreference-chains/) 2. Bakir, G.; Hofmann, T.; Schölkopf, B.; Smola, A.; Taskar, B. and Vishwanathan, S. (eds.) 2007. Predicting Structured Data. MIT Press. [find](https://scholar.google.com/scholar?q=Bakir%2C+G%2E%3B+Hofmann%2C+T%2E%3B+Scho%CC%88lkopf%2C+B%2E%3B+Smola%2C+A%2E%3B+Taskar%2C+B%2E+and+Vishwanathan%2C+S%2E+%28eds%2E%29+2007%2E+Predicting+Structured+Data%2E+MIT+Press%2E) 3. Culotta, A.; Wick, M.; Hall, R. and McCallum, A. 2007. First-order probabilistic models for coreference resolution. In Proc. NAACL-07. [find](https://scholar.google.com/scholar?q=Culotta%2C+A%2E%3B+Wick%2C+M%2E%3B+Hall%2C+R%2E+and+McCallum%2C+A%2E+2007%2E+First%2Dorder+probabilistic+models+for+coreference+resolution%2E+In+Proc%2E+NAACL%2D07%2E) [our card](https://md.research.elamur.ai/free/papers/culotta-2007-first-order-probabilistic-models-coreference/) 4. Denis, P. & Baldridge, J. 2007. Joint determination of anaphoricity and coreference resolution using integer programming. In Proc. NAACL-07. [find](https://scholar.google.com/scholar?q=Denis%2C+P%2E+%26+Baldridge%2C+J%2E+2007%2E+Joint+determination+of+anaphoricity+and+coreference+resolution+using+integer+programming%2E+In+Proc%2E+NAACL%2D07%2E) [our card](https://md.research.elamur.ai/free/papers/denis-2007-joint-determination-anaphoricity-coreference-resolution/) 5. Getoor, L. & Taskar, B. (eds.) 2007. Introduction to Statistical Relational Learning. MIT Press. [find](https://scholar.google.com/scholar?q=Getoor%2C+L%2E+%26+Taskar%2C+B%2E+%28eds%2E%29+2007%2E+Introduction+to+Statistical+Relational+Learning%2E+MIT+Press%2E) 6. Haghighi, A. & Klein, D. 2007. Unsupervised coreference resolution in a nonparametric Bayesian model. In Proc. ACL-07. [find](https://scholar.google.com/scholar?q=Haghighi%2C+A%2E+%26+Klein%2C+D%2E+2007%2E+Unsupervised+coreference+resolution+in+a+nonparametric+Bayesian+model%2E+In+Proc%2E+ACL%2D07%2E) [our card](https://md.research.elamur.ai/free/papers/haghighi-2007-unsupervised-coreference-resolution-nonparametric-bayesian/) 7. Kautz, H.; Selman, B.; and Jiang, Y. 1997. A general stochastic approach to solving problems with hard and soft constraints. In The Satisfiability Problem: Theory and Applications. AMS. [find](https://scholar.google.com/scholar?q=Kautz%2C+H%2E%3B+Selman%2C+B%2E%3B+and+Jiang%2C+Y%2E+1997%2E+A+general+stochastic+approach+to+solving+problems+with+hard+and+soft+constraints%2E+In+The+Satisfiability+Problem%3A+Theory+and+Applications%2E+AMS%2E) 8. Klein, D. & Manning, C. 2003. Accurate unlexicalized parsing. In Proc. ACL-03. [find](https://scholar.google.com/scholar?q=Klein%2C+D%2E+%26+Manning%2C+C%2E+2003%2E+Accurate+unlexicalized+parsing%2E+In+Proc%2E+ACL%2D03%2E) [our card](https://md.research.elamur.ai/free/papers/aclbulk-2003-klein-accurate-unlexicalized-parsing/) 9. Kok, S.; Singla, P.; Richardson, M.; Domingos, P.; Sumner, M.; Poon, H. & Lowd, D. 2007. The Alchemy system for statistical relational AI. [http://alchemy.cs.washington.edu/](http://alchemy.cs.washington.edu/). [↗](http://alchemy.cs.washington.edu/) 10. Lowd, D. & Domingos, D. 2007. Efficient weight learning for Markov logic networks. In Proc. PKDD-07. [find](https://scholar.google.com/scholar?q=Lowd%2C+D%2E+%26+Domingos%2C+D%2E+2007%2E+Efficient+weight+learning+for+Markov+logic+networks%2E+In+Proc%2E+PKDD%2D07%2E) 11. Luo, X.; Ittycheriah, A.; Jing, H.; Kambhatla, N. and Roukos, S. 2004. A mention-synchronous coreference resolution algorithm based on the bell tree. In Proc. ACL-04. [find](https://scholar.google.com/scholar?q=Luo%2C+X%2E%3B+Ittycheriah%2C+A%2E%3B+Jing%2C+H%2E%3B+Kambhatla%2C+N%2E+and+Roukos%2C+S%2E+2004%2E+A+mention%2Dsynchronous+coreference+resolution+algorithm+based+on+the+bell+tree%2E+In+Proc%2E+ACL%2D04%2E) [our card](https://md.research.elamur.ai/free/papers/luo-2004-mention-synchronous-coreference-resolution-algorithm/) 12. McCallum, A. & Wellner, B. 2005. Conditional models of identity uncertainty with application to noun coreference. In Proc. NIPS-04. [find](https://scholar.google.com/scholar?q=McCallum%2C+A%2E+%26+Wellner%2C+B%2E+2005%2E+Conditional+models+of+identity+uncertainty+with+application+to+noun+coreference%2E+In+Proc%2E+NIPS%2D04%2E) 13. Ng, V. 2005. Machine Learning for Coreference Resolution: From Local Classification to Global Ranking. In Proc. ACL-05. [find](https://scholar.google.com/scholar?q=Ng%2C+V%2E+2005%2E+Machine+Learning+for+Coreference+Resolution%3A+From+Local+Classification+to+Global+Ranking%2E+In+Proc%2E+ACL%2D05%2E) [our card](https://md.research.elamur.ai/free/papers/ng-2005-machine-learning-coreference-resolution-local/) 14. Poon, H. & Domingos, P. 2006. Sound and efficient inference with probabilistic and deterministic dependencies. In Proc. AAAI-06. [find](https://scholar.google.com/scholar?q=Poon%2C+H%2E+%26+Domingos%2C+P%2E+2006%2E+Sound+and+efficient+inference+with+probabilistic+and+deterministic+dependencies%2E+In+Proc%2E+AAAI%2D06%2E) 15. Poon, H. & Domingos, P. 2007. Joint inference in information extraction. In Proc. AAAI-07. [find](https://scholar.google.com/scholar?q=Poon%2C+H%2E+%26+Domingos%2C+P%2E+2007%2E+Joint+inference+in+information+extraction%2E+In+Proc%2E+AAAI%2D07%2E) 16. Poon, H. & Domingos, P. 2008. A general method for reducing the complexity of relational inference and its application to MCMC. In Proc. AAAI-08. [find](https://scholar.google.com/scholar?q=Poon%2C+H%2E+%26+Domingos%2C+P%2E+2008%2E+A+general+method+for+reducing+the+complexity+of+relational+inference+and+its+application+to+MCMC%2E+In+Proc%2E+AAAI%2D08%2E) 17. Richardson, M. & Domingos, P. 2006. Markov logic networks. Machine Learning 62:107–136. [find](https://scholar.google.com/scholar?q=Richardson%2C+M%2E+%26+Domingos%2C+P%2E+2006%2E+Markov+logic+networks%2E+Machine+Learning+62%3A107%E2%80%93136%2E) 18. Vilain, M.; Burger, J.; Aberdeen, J.; Connolly, D. & Hirschman, L. 1995. A model-theoretic coreference scoring scheme. In Proc. MUC-6. [find](https://scholar.google.com/scholar?q=Vilain%2C+M%2E%3B+Burger%2C+J%2E%3B+Aberdeen%2C+J%2E%3B+Connolly%2C+D%2E+%26+Hirschman%2C+L%2E+1995%2E+A+model%2Dtheoretic+coreference+scoring+scheme%2E+In+Proc%2E+MUC%2D6%2E) [our card](https://md.research.elamur.ai/free/papers/vilain-1995-model-theoretic-coreference-scoring-scheme/) 19. Wei, W.; Erenrich, J. and Selman, B. 2004. Towards efficient sampling: Exploiting random walk strategies. In Proc. AAAI-04. [find](https://scholar.google.com/scholar?q=Wei%2C+W%2E%3B+Erenrich%2C+J%2E+and+Selman%2C+B%2E+2004%2E+Towards+efficient+sampling%3A+Exploiting+random+walk+strategies%2E+In+Proc%2E+AAAI%2D04%2E)