--- title: "Learning-based Multi-Sieve Co-reference — Ratinov 2012" description: "The paper tackles co-reference resolution: deciding which mentions in a text point to the same entity." authors: ["Lev Ratinov", "Dan Roth"] year: 2012 venue: "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning" source: "https://aclanthology.org/D12-1113/" 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/ratinov-2012-learning-based-multi-sieve-co/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/ratinov-2012-learning-based-multi-sieve-co/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2012](https://md.research.elamur.ai/free/papers/2012/) / Learning-based Multi-Sieve Co-reference Resolution with Knowledge # Learning-based Multi-Sieve Co-reference Resolution with Knowledge **Authors:** [Lev Ratinov](https://md.research.elamur.ai/free/authors/lev-ratinov/), [Dan Roth](https://md.research.elamur.ai/free/authors/dan-roth/) · **Year:** 2012 · **Venue:** Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning · **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/D12-1113/) · [PDF](https://aclanthology.org/D12-1113.pdf) · [OpenAlex](https://openalex.org/W85083507) **Citations:** 46 citations (openalex-snapshot · 2026-10-14) · [26](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper tackles co-reference resolution: deciding which mentions in a text point to the same entity. Many errors come from missing world knowledge, such as not knowing that "Kursk" is a submarine and can therefore be called a "vessel." The authors link mentions to Wikipedia pages using an existing context-sensitive disambiguation system. From the linked pages they derive a small, high-precision set of attributes: fine-grained semantic categories, gender and nationality. These attributes feed knowledge-based compatibility features in the pairwise model of Bengtson and Roth (2008). The authors also recast rule-based multi-sieve resolution as a sequence of learned classifiers ordered from easy to hard decisions, with two key properties: - a later sieve may revise an earlier decision; - entity-based features let mentions that have already been grouped share evidence. On the non-transcript part of ACE 2004, the full system beats this strong baseline by about 3 MUC, 2 B3 and 2 CEAF F1 points. The work shows that disambiguated encyclopedic knowledge and staged learned inference can improve a competitive resolver. ## Key points - **Baseline:** Bengtson and Roth (2008), a pairwise averaged-perceptron scorer with Best-Link inference. It had the top B3 and BLANC scores at the CoNLL 2011 shared task. On the authors' split it reaches roughly 78.39 MUC, 83.03 B3 and 77.52 CEAF. - **Category extraction:** A simple heuristic takes category heads (plural nouns before relation words like "of" or "in") and drops rare tokens. This yields 2088 fine-grained types, which are manually mapped to ACE-style classes (ORG, LOC, GPE, FACILITY). - **Gender:** Gender is inferred from the pronouns in each article's first paragraph. - **Nationality:** Nationality is taken from country names in the page's categories. - **Robustness of gains:** The improvements hold across all decision thresholds, with a minimum of 3 MUC, 2 B3 and 1.25 CEAF F1. The extracted attributes are released publicly. - **Limitations:** The system relies on gold mention boundaries and exhaustive annotation, so it was not applied to OntoNotes. The authors also describe their division into sieves as preliminary. *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 26 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 15 not yet. - [A Constrained Latent Variable Model for Coreference Resolution](https://md.research.elamur.ai/free/papers/chang-2013-constrained-latent-variable-model-coreference/) - [A Joint Model for Entity Analysis: Coreference, Typing, and Linking](https://md.research.elamur.ai/free/papers/durrett-2014-joint-model-entity-analysis-coreference/) - [Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment](https://md.research.elamur.ai/free/papers/dutta-2015-cross-document-co-reference-resolution/) - [Coreference in Wikipedia: Main Concept Resolution](https://md.research.elamur.ai/free/papers/ghaddar-2016-coreference-wikipedia-main-concept-resolution/) - [Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers](https://md.research.elamur.ai/free/papers/guha-2015-removing-training-wheels-coreference-dataset/) - [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/) - [Prune-and-Score: Learning for Greedy Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2014-prune-score-learning-greedy-coreference/) - [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2016-unsupervised-ranking-model-entity-coreference/) - [Recall Error Analysis for Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2014-recall-error-analysis-coreference-resolution/) - [Solving Hard Coreference Problems](https://md.research.elamur.ai/free/papers/peng-2015-solving-hard-coreference-problems/) - [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{ratinov2012learning, title = {{Learning-based Multi-Sieve Co-reference Resolution with Knowledge}}, author = {Ratinov, Lev and Roth, Dan}, booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning}, year = {2012}, url = {https://aclanthology.org/D12-1113/} } ``` - [← previous Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution](https://md.research.elamur.ai/free/papers/fernandes-2012-latent-structure-perceptron-feature-induction/) - [next → Lemmatisation as a Tagging Task](https://md.research.elamur.ai/free/papers/aclbulk-2012-gesmundo-lemmatisation-as-tagging-task/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/papers/2012/) ## References 2 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. A. Bagga and B. Baldwin. 1998. Algorithms for scoring coreference chains. In MUC7. [find](https://scholar.google.com/scholar?q=A%2E+Bagga+and+B%2E+Baldwin%2E+1998%2E+Algorithms+for+scoring+coreference+chains%2E+In+MUC7%2E) [our card](https://md.research.elamur.ai/free/papers/bagga-1998-algorithms-scoring-coreference-chains/) 2. E. Bengtson and D. Roth. 2008. Understanding the value of features for coreference resolution. In EMNLP. [find](https://scholar.google.com/scholar?q=E%2E+Bengtson+and+D%2E+Roth%2E+2008%2E+Understanding+the+value+of+features+for+coreference+resolution%2E+In+EMNLP%2E) [our card](https://md.research.elamur.ai/free/papers/bengtson-2008-understanding-value-features-coreference-resolution/)