--- title: "Data-driven Multilingual Coreference — Björkelund 2012" description: "This CoNLL 2012 Shared Task system performs coreference resolution for Arabic, Chinese and English." authors: ["Anders Björkelund", "Richárd Farkas"] year: 2012 venue: "Joint Conference on EMNLP and CoNLL - Shared Task" source: "https://aclanthology.org/W12-4503/" 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/bjorkelund-2012-data-driven-multilingual-coreference-resolution/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bjorkelund-2012-data-driven-multilingual-coreference-resolution/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2012](https://md.research.elamur.ai/free/en/papers/2012/) / Data-driven Multilingual Coreference Resolution using Resolver Stacking # Data-driven Multilingual Coreference Resolution using Resolver Stacking **Authors:** [Anders Björkelund](https://md.research.elamur.ai/free/en/authors/anders-bjorkelund/), [Richárd Farkas](https://md.research.elamur.ai/free/en/authors/richard-farkas/) · **Year:** 2012 · **Venue:** Joint Conference on EMNLP and CoNLL - Shared Task · **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/W12-4503/) · [PDF](https://aclanthology.org/W12-4503.pdf) · [OpenAlex](https://openalex.org/W2121636384) **Citations:** 40 citations (openalex-snapshot · 2026-10-14) · [21](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This CoNLL 2012 Shared Task system performs coreference resolution for Arabic, Chinese and English. Its goal is to reduce a known weakness of pair-wise resolvers, which judge each mention pair in isolation. The pipeline begins with a deliberately over-generating mention extractor built on parse trees and, for English, named entities. In English, a MaxEnt filter then removes clearly non-referential uses of *it*, *we* and *you*. A LIBLINEAR logistic regression model scores mention pairs, using Soon et al.-style training instances. The authors add a new cluster-aware decoder, AverageMaxProb (AMP). It links a mention to the existing cluster that has the highest geometric-mean link probability across all of its members. AMP yields smaller but more consistent clusters. Its output then becomes an extra feature for a second, conventional pair-wise decoder (BF or PCF), in a stacking setup. Decoders also block clusters formed by transitively merging nested mentions. The stacked system scored 58.25 officially, second among sixteen teams. This shows that cluster-level and pair-wise decoding complement each other across typologically different languages. ## Key points - **Official result:** 58.25 overall (mean of MUC, B3 and CEAFE over three languages), ranked 2nd of 16. Test CoNLL scores with predicted mentions were Arabic 53.55, Chinese 59.97 and English 61.24. - **English pronoun filter:** separate Mallet MaxEnt classifiers handle *it*, *we* and *you*. A strict 0.95 posterior threshold keeps false removals rare and added about 0.5 CoNLL points. Similar filters for Arabic and Chinese brought no benefit. - **Nesting constraint:** forbidding transitive clustering of nested mentions improved English by roughly 0.6 points, while Arabic and Chinese each rose by around 0.4. - **Stacking design:** - AMP runs first with a fixed 0.5 threshold. - The second resolver receives a five-valued feature describing the first resolver's decision (NEITHER, IONLY, JONLY, COREF, DISREF). - Arabic uses PCF at 0.60, Chinese uses BF at 0.65, and English uses PCF at 0.65. - Stacking two pair-wise decoders gave no improvement. - **Development-set CoNLL scores (Stacked):** Arabic 51.84, Chinese 60.30 and English 61.71, each beating the BF, PCF and AMP decoders used alone. The final models were trained on train+dev, which notably boosted Arabic on the test set. *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/en/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cited by Cited by 21 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 8 not yet. - [Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron](https://md.research.elamur.ai/free/en/papers/araki-2015-joint-event-trigger-identification-event/) - [Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features](https://md.research.elamur.ai/free/en/papers/bjorkelund-2014-learning-structured-perceptrons-coreference-resolution/) - [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/en/papers/clark-2015-entity-centric-coreference-resolution-model/) - [Easy Victories and Uphill Battles in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/durrett-2013-easy-victories-uphill-battles-coreference/) - [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/fernandes-2014-latent-trees-coreference-resolution/) - [Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers](https://md.research.elamur.ai/free/en/papers/guha-2015-removing-training-wheels-coreference-dataset/) - [Exploiting Zero Pronouns to Improve Chinese Coreference Resolution](https://md.research.elamur.ai/free/en/papers/kong-2013-exploiting-zero-pronouns-improve-chinese/) - [Error-Driven Analysis of Challenges in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/kummerfeld-2013-error-driven-analysis-challenges-coreference/) - [Recall Error Analysis for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/martschat-2014-recall-error-analysis-coreference-resolution/) - [Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers](https://md.research.elamur.ai/free/en/papers/moosavi-2018-linguistic-features-improve-generalization-capability/) - [Coreference Resolution Evaluation for Higher Level Applications](https://md.research.elamur.ai/free/en/papers/tuggener-2014-coreference-resolution-evaluation-higher-level/) - [The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors](https://md.research.elamur.ai/free/en/papers/vala-2016-more-antecedents-merrier-resolving-multi/) - [Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{bjorkelund2012data, title = {{Data-driven Multilingual Coreference Resolution using Resolver Stacking}}, author = {Björkelund, Anders and Farkas, Richárd}, booktitle = {Joint Conference on EMNLP and CoNLL - Shared Task}, year = {2012}, url = {https://aclanthology.org/W12-4503/} } ``` - [← previous Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure](https://md.research.elamur.ai/free/en/papers/tackstrom-2012-cross-lingual-word-clusters-direct/) - [next → Developing a large semantically annotated corpus](https://md.research.elamur.ai/free/en/papers/basile-2012-developing-large-semantically-annotated-corpus/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/en/papers/2012/) ## References 12 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Anders Björkelund and Pierre Nugues. 2011. Exploring lexicalized features for coreference resolution. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, pages 45–50, June. [find](https://scholar.google.com/scholar?q=Anders+Bjo%CC%88rkelund+and+Pierre+Nugues%2E+2011%2E+Exploring+lexicalized+features+for+coreference+resolution%2E+In+Proceedings+of+the+Fifteenth+Conference+on+Computational+Natural+Language+Learning%3A+Shared+Tas) 2. Adriane Boyd, Whitney Gegg-Harrison, and Donna Byron. 2005. Identifying non-referential it: A machine learning approach incorporating linguistically motivated patterns. In Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing, pages 40–47, June. 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[http://mallet.cs.umass.edu](http://mallet.cs.umass.edu). [↗](http://mallet.cs.umass.edu) 7. Eugene W. Myers. 1986. An O(ND) difference algorithm and its variations. Algorithmica, 1:251–266. [find](https://scholar.google.com/scholar?q=Eugene+W%2E+Myers%2E+1986%2E+An+O%28ND%29+difference+algorithm+and+its+variations%2E+Algorithmica%2C+1%3A251%E2%80%93266%2E) 8. Vincent Ng. 2010. Supervised noun phrase coreference research: The first fifteen years. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1396–1411, July. [find](https://scholar.google.com/scholar?q=Vincent+Ng%2E+2010%2E+Supervised+noun+phrase+coreference+research%3A+The+first+fifteen+years%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+1396%E2%80%931411%2C+Jul) [our card](https://md.research.elamur.ai/free/en/papers/ng-2010-supervised-noun-phrase-coreference-research/) 9. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. In Proceedings of the Sixteenth Conference on Computational Natural Language Learning (CoNLL 2012). [find](https://scholar.google.com/scholar?q=Sameer+Pradhan%2C+Alessandro+Moschitti%2C+Nianwen+Xue%2C+Olga+Uryupina%2C+and+Yuchen+Zhang%2E+2012%2E+CoNLL%2D2012+shared+task%3A+Modeling+multilingual+unrestricted+coreference+in+OntoNotes%2E+In+Proceedings+of+the+Six) [our card](https://md.research.elamur.ai/free/en/papers/pradhan-2012-conll-2012-shared-task-modeling/) 10. Emili Sapena, Lluı́s Padró, and Jordi Turmo. 2010. A global relaxation labeling approach to coreference resolution. In Coling 2010: Posters, pages 1086–1094, August. 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