--- title: "SinoCoreferencer — Chen 2014" description: "Event coreference resolution groups event mentions in a text that describe the same real-world happening." authors: ["Chen Chen", "Vincent Ng"] year: 2014 venue: "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)" source: "https://aclanthology.org/L14-1099/" 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-2014-sinocoreferencer-end-end-chinese-event/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/chen-2014-sinocoreferencer-end-end-chinese-event/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2014](https://md.research.elamur.ai/free/papers/2014/page/2/) / SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver # SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver **Authors:** [Chen Chen](https://md.research.elamur.ai/free/authors/chen-chen/), [Vincent Ng](https://md.research.elamur.ai/free/authors/vincent-ng/) · **Year:** 2014 · **Venue:** Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) · **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/L14-1099/) · [PDF](https://aclanthology.org/L14-1099.pdf) · [OpenAlex](https://openalex.org/W2250637647) **Citations:** 23 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Event coreference resolution groups event mentions in a text that describe the same real-world happening. It sits at the very end of an information extraction pipeline, and almost all prior systems target English. The authors present SinoCoreferencer, an end-to-end resolver for Chinese that follows the ACE task definitions and is released publicly. Raw text is first preprocessed with Stanford CoreNLP. It then flows through eight modules grouped into four subsystems: entity extraction, entity coreference, event extraction and event coreference. Each module can also be run on its own. The modules use character-level CRF sequence labelers (CRF++), SVM multiclass classifiers and a rule-based multi-pass sieve resolver for entities. Evaluated on the ACE 2005 Chinese data, the system reports state-of-the-art event coreference, scoring 73.4 average F with gold inputs and 49.9 with fully predicted inputs. Comparing these two settings shows how much errors accumulate along the pipeline. The authors believe it is the first publicly available Chinese event coreference tool, and its reusable components can support other Chinese NLP applications. ## Key points - The architecture has eight components in four subsystems. Event coreference depends on the outputs of the event extraction and entity coreference subsystems, and both of those depend on entity extraction. - Entity mentions, time expressions and value expressions are found with character-based BIO tagging in CRF++. The features include character n-grams, 10 Chinese wordlists and POS/NP cues, and this step reaches 84.7% F. - Entity typing covers the 7 ACE 2005 types and 45 subtypes. The named entity tagger is trained on CoNLL-2012 Chinese data with 18 NE types (37 labels) and scores 66.4% F. - Entity coreference uses nine ordered sieves, partly modeled on Lee et al. (2011). It achieves 70.6 Avg F (MUC/B³/CEAFe) with gold input and 63.0 with predicted input. - Event coreference scores 73.4 Avg F with perfect input but 49.9 with predicted input. Argument identification drops even more sharply, from 76.9% to 28.3% F. *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 9 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 5 not yet. - [Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers](https://md.research.elamur.ai/free/papers/chen-2014-chinese-zero-pronoun-resolution-unsupervised/) - [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/) - [A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution](https://md.research.elamur.ai/free/papers/lai-2021-context-dependent-gated-module-incorporating/) - [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{chen2014sinocoreferencer, title = {{SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver}}, author = {Chen, Chen and Ng, Vincent}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, doi = {10.63317/57n7frh2boyy}, url = {https://doi.org/10.63317/57n7frh2boyy} } ``` - [← previous Show and Tell: A Neural Image Caption Generator](https://md.research.elamur.ai/free/papers/vinyals-2014-show-tell-neural-image-caption/) - [next → Stochastic Backpropagation and Approximate Inference in Deep Generative Models](https://md.research.elamur.ai/free/papers/rezende-2014-stochastic-backpropagation-approximate-inference-deep/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/page/2/) ## References 18 references, 13 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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