--- title: "Supervised Within-Document Event Coreference — Liu 2014" description: "The paper addresses within-document event coreference, which means deciding when two event mentions in one text describe the same real-world event." authors: ["Zhengzhong Liu", "Jun Araki", "Eduard Hovy", "Teruko Mitamura"] year: 2014 venue: "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)" source: "https://aclanthology.org/L14-1513/" 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/liu-2014-supervised-within-document-event-coreference/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/liu-2014-supervised-within-document-event-coreference/) [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/) / Supervised Within-Document Event Coreference using Information Propagation # Supervised Within-Document Event Coreference using Information Propagation **Authors:** [Zhengzhong Liu](https://md.research.elamur.ai/free/authors/zhengzhong-liu/), [Jun Araki](https://md.research.elamur.ai/free/authors/jun-araki/), [Eduard Hovy](https://md.research.elamur.ai/free/authors/eduard-hovy/), [Teruko Mitamura](https://md.research.elamur.ai/free/authors/teruko-mitamura/) · **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/L14-1513/) · [PDF](https://aclanthology.org/L14-1513.pdf) · [OpenAlex](https://openalex.org/W2252089544) **Citations:** 57 citations (openalex-snapshot · 2026-10-14) · [33](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper addresses within-document event coreference, which means deciding when two event mentions in one text describe the same real-world event. The authors first survey earlier work. They argue that past results cannot be compared fairly because studies differ in how they define coreference, use partially annotated corpora such as ECB, rely on gold ACE arguments and attributes, or use data that is not public. They then build a supervised pairwise system. A Random Forest classifier in Weka scores mention pairs, and BestLink clustering groups them. The system uses lexical, discourse, syntactic and semantic features, along with event arguments (agent, patient, location). After clustering, argument information is passed between coreferent mentions, and the handling differs by argument type. For locations, the system adds world knowledge from DBpedia and Geonames. Experiments on the IC corpus show that arguments and propagation improve most metrics. On ACE 2005, the system scores above previously reported systems, although the authors stress that these comparisons are only approximate. Pairwise F stays near 50%, so the task remains far from solved. ## Key points - **Data:** The main dataset is the IC domain corpus of 65 documents, split into 40 for training/development and 25 for testing. Full-coreference inter-annotator agreement is 0.614 (Fleiss's kappa). The system is also evaluated on ACE 2005, using 539 documents for training and 60 for testing. - **Pipeline and resources:** - Arguments come from the Fanse semantic parser, using PropBank ARG0/ARG1. - Locations come from Stanford NER and are disambiguated with DBpedia Spotlight, then DBpedia, with Geonames as a fallback. This lets the system link, for example, Istanbul to Turkey. - The feature set includes 105 semantic features. - **IC results:** Adding propagation raises pairwise F from 51.60 to 53.11, MUC F to 61.44, and BLANC F to 75.06. The gain in recall comes with some loss of precision. In practice, propagation ran for only one iteration. - **ACE results:** The system reaches MUC F 50.98, B³ F 89.38 and CEAF-e F 86.47. Bejan & Harabagiu (2010) reported 45.1, 83.8 and 76.7 on the same metrics. - **Clustering:** BestLink consistently beats plain transitive closure. Errors often come from over-merging mentions that share a head word and from imperfect argument extraction. *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 33 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 20 not yet. - [Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms](https://md.research.elamur.ai/free/papers/aclbulk-2016-dean-verb-phrase-ellipsis-resolution-discriminative/) - [Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron](https://md.research.elamur.ai/free/papers/araki-2015-joint-event-trigger-identification-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/) - [Cross-Document Event Coreference Resolution on Discourse Structure](https://md.research.elamur.ai/free/papers/chen-2023-cross-document-event-coreference-resolution/) - [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://md.research.elamur.ai/free/papers/choubey-2017-event-coreference-resolution-iteratively-unfolding/) - [Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures](https://md.research.elamur.ai/free/papers/choubey-2018-improving-event-coreference-resolution-modeling/) - [Joint Inference for Event Coreference Resolution](https://md.research.elamur.ai/free/papers/lu-2016-joint-inference-event-coreference-resolution/) - [Constrained Multi-Task Learning for Event Coreference Resolution](https://md.research.elamur.ai/free/papers/lu-2021-constrained-multi-task-learning-event/) - [Conundrums in Event Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/papers/lu-2021-conundrums-event-coreference-resolution-making/) - [Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution](https://md.research.elamur.ai/free/papers/tran-2021-exploiting-document-structures-cluster-consistencies/) - [Revisiting the Evaluation for Cross Document Event Coreference](https://md.research.elamur.ai/free/papers/upadhyay-2016-revisiting-evaluation-cross-document-event/) - [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://md.research.elamur.ai/free/papers/wang-2022-maven-ere-unified-large-scale/) - [Improving Event Coreference Resolution Using Document-level and Topic-level Information](https://md.research.elamur.ai/free/papers/xu-2022-improving-event-coreference-resolution-document/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{liu2014supervised, title = {{Supervised Within-Document Event Coreference using Information Propagation}}, author = {Liu, Zhengzhong and Araki, Jun and Hovy, Eduard and Mitamura, Teruko}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, doi = {10.63317/38fv3yfzncaa}, url = {https://doi.org/10.63317/38fv3yfzncaa} } ``` - [← previous Summarizing Online Forum Discussions – Can Dialog Acts of Individual Messages Help?](https://md.research.elamur.ai/free/papers/bhatia-2014-summarizing-online-forum-discussions-can/) - [next → Tailoring Continuous Word Representations for Dependency Parsing](https://md.research.elamur.ai/free/papers/bansal-2014-tailoring-continuous-word-representations-dependency/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/page/2/) ## References 7 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Ahn, D. (2006). The stages of event extraction. ARTE ’06 Proceedings of the Workshop on Annotating and Reasoning about Time and Events, 1–8. [find](https://scholar.google.com/scholar?q=Ahn%2C+D%2E+%282006%29%2E+The+stages+of+event+extraction%2E+ARTE+%E2%80%9906+Proceedings+of+the+Workshop+on+Annotating+and+Reasoning+about+Time+and+Events%2C+1%E2%80%938%2E) 2. Bagga, A., & Baldwin, B. (1998). Algorithms for scoring coreference chains. In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference, 563–566. [find](https://scholar.google.com/scholar?q=Bagga%2C+A%2E%2C+%26+Baldwin%2C+B%2E+%281998%29%2E+Algorithms+for+scoring+coreference+chains%2E+In+The+First+International+Conference+on+Language+Resources+and+Evaluation+Workshop+on+Linguistics+Coreference%2C+563%E2%80%93566%2E) [our card](https://md.research.elamur.ai/free/papers/bagga-1998-algorithms-scoring-coreference-chains/) 3. Bagga, A., & Baldwin, B. (1999). Cross - Document Event Coreference: Annotations, Experiments, and Observations. ACL-99 Workshop on Coreference and Its Applications. [find](https://scholar.google.com/scholar?q=Bagga%2C+A%2E%2C+%26+Baldwin%2C+B%2E+%281999%29%2E+Cross+%2D+Document+Event+Coreference%3A+Annotations%2C+Experiments%2C+and+Observations%2E+ACL%2D99+Workshop+on+Coreference+and+Its+Applications%2E) [our card](https://md.research.elamur.ai/free/papers/bagga-1999-cross-document-event-coreference-annotations/) 4. Bejan, C., & Harabagiu, S. (2010). Unsupervised event coreference resolution with rich linguistic features. Proceedings of the 48th Annual Meeting of the Association for the Association for Computational Linguistics(July), 1412–1422. [find](https://scholar.google.com/scholar?q=Bejan%2C+C%2E%2C+%26+Harabagiu%2C+S%2E+%282010%29%2E+Unsupervised+event+coreference+resolution+with+rich+linguistic+features%2E+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+the+Association+for+Computatio) [our card](https://md.research.elamur.ai/free/papers/bejan-2010-unsupervised-event-coreference-resolution-rich/) 5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. [find](https://scholar.google.com/scholar?q=Breiman%2C+L%2E+%282001%29%2E+Random+forests%2E+Machine+Learning%2C+45%281%29%2C+5%2D32%2E) 6. Chen, Z., & Ji, H. (2009). Graph-based event coreference resolution. Proceedings of the 2009 Workshop on Type (counts) [find](https://scholar.google.com/scholar?q=Chen%2C+Z%2E%2C+%26+Ji%2C+H%2E+%282009%29%2E+Graph%2Dbased+event+coreference+resolution%2E+Proceedings+of+the+2009+Workshop+on+Type+%28counts%29) [our card](https://md.research.elamur.ai/free/papers/chen-2009-graph-based-event-coreference-resolution/) 7. Feature Name Sentence Distance Event Distance Position Event String Similarity [find](https://scholar.google.com/scholar?q=Feature+Name+Sentence+Distance+Event+Distance+Position+Event+String+Similarity)