--- title: "A Unified Event Coreference Resolution by — Chen 2011" description: "Most coreference work targets objects such as people or places." authors: ["Bin Chen", "Jian Su", "Sinno Jialin Pan", "Chew Lim Tan"] year: 2011 venue: "Proceedings of 5th International Joint Conference on Natural Language Processing" source: "https://aclanthology.org/I11-1012/" 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/chen-2011-unified-event-coreference-resolution-integrating/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/chen-2011-unified-event-coreference-resolution-integrating/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2011](https://md.research.elamur.ai/free/papers/2011/) / A Unified Event Coreference Resolution by Integrating Multiple Resolvers # A Unified Event Coreference Resolution by Integrating Multiple Resolvers **Authors:** [Bin Chen](https://md.research.elamur.ai/free/authors/bin-chen/), [Jian Su](https://md.research.elamur.ai/free/authors/jian-su/), [Sinno Jialin Pan](https://md.research.elamur.ai/free/authors/sinno-jialin-pan/), [Chew Lim Tan](https://md.research.elamur.ai/free/authors/chew-lim-tan/) · **Year:** 2011 · **Venue:** Proceedings of 5th International Joint Conference on 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/I11-1012/) · [PDF](https://aclanthology.org/I11-1012.pdf) · [OpenAlex](https://openalex.org/W2145164276) **Citations:** 27 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Most coreference work targets objects such as people or places. This paper focuses on event coreference, where verbs, noun phrases and pronouns can all point to the same eventuality. The task is harder than object coreference because events are distinguished by their arguments (time, place, participants), and standard coreference features work poorly when mentions belong to different syntactic categories. Working on OntoNotes 2.0, the authors build a two-step system: 1. Seven mention-pair resolvers, three of them new, cover both event and object cases. They use SVMs with a composite kernel that combines flat features with a convolution tree kernel over the minimum-expansion parse path. 2. Chains are then formed with spectral graph partitioning. Three enhancements are added on top: - Object resolvers' outputs are used to reject spurious event links. - Training instances are reselected so that classifiers predict links between any two mentions, not just local candidate preferences. - Coreferent pronoun information is fed into the partitioning step. Together these raise event-chain B3 F-score from 38.33% to 46.91%. The work matters because event extraction systems need such chains to gather event details scattered across sentences. ## Key points - In OntoNotes 2.0, 68.05% of documents have at least one event chain, and event chains make up 15.52% of all coreference chains. - Using competing object-coreference classifiers improves the Verb-Pronoun resolver by more than 9 F points and the event NP-Pronoun resolver by about 7. Chain-level B3 F rises from 38.33% to 43.61%. - Revised instance selection has mixed effects at the pair level but helps chains: - Best-candidate F falls by 3.26 points. - Coreferent-link F rises by 2.84 points. - B3 chain F improves by 2.02 points (to 45.63%). - Adding pronoun information to spectral partitioning gives +2.19 F (45.82%). Both this gain and the instance-selection gain are significant under a paired t-test at the 5% level. - The best configuration (BL+CC+RIS+Pron) reaches B3 P/R/F of 35.27/70.02/46.91%, an overall gain of 8.54 points. *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 16 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 9 not yet. - [Chinese Event Coreference Resolution: Understanding the State of the Art](https://md.research.elamur.ai/free/papers/chen-2013-chinese-event-coreference-resolution-understanding/) - [SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver](https://md.research.elamur.ai/free/papers/chen-2014-sinocoreferencer-end-end-chinese-event/) - [Chinese Event Coreference Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers](https://md.research.elamur.ai/free/papers/chen-2015-chinese-event-coreference-resolution-unsupervised/) - [Semantic Relations between Events and their Time, Locations and Participants for Event Coreference Resolution](https://md.research.elamur.ai/free/papers/cybulska-2013-semantic-relations-between-events-their/) - [Using a sledgehammer to crack a nut? Lexical diversity and event coreference resolution](https://md.research.elamur.ai/free/papers/cybulska-2014-sledgehammer-crack-nut-lexical-diversity/) - [Improving pairwise coreference models through feature space hierarchy learning](https://md.research.elamur.ai/free/papers/lassalle-2013-improving-pairwise-coreference-models-through/) - [Joint Inference for Event Coreference Resolution](https://md.research.elamur.ai/free/papers/lu-2016-joint-inference-event-coreference-resolution/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{chen2011unified, title = {{A Unified Event Coreference Resolution by Integrating Multiple Resolvers}}, author = {Chen, Bin and Su, Jian and Pan, Sinno Jialin and Tan, Chew Lim}, booktitle = {Proceedings of 5th International Joint Conference on Natural Language Processing}, year = {2011}, url = {https://aclanthology.org/I11-1012/} } ``` - [← previous A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models](https://md.research.elamur.ai/free/papers/li-2011-pronoun-anaphora-resolution-system-based/) - [next → An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue](https://md.research.elamur.ai/free/papers/boyer-2011-affect-enriched-dialogue-act-classification/) - [↑ back to papers of 2011](https://md.research.elamur.ai/free/papers/2011/) ## References 17 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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