--- title: "Coreference Resolution for Information — Zelenko 2004" description: "This paper addresses coreference resolution as a component of information extraction (IE)." authors: ["Dmitry Zelenko", "Chinatsu Aone", "Jason Tibbetts"] year: 2004 venue: "Proceedings of the Conference on Reference Resolution and Its Applications" source: "https://aclanthology.org/W04-0704/" 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/zelenko-2004-coreference-resolution-information-extraction/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/zelenko-2004-coreference-resolution-information-extraction/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2004](https://md.research.elamur.ai/free/papers/2004/) / Coreference Resolution for Information Extraction # Coreference Resolution for Information Extraction **Authors:** Dmitry Zelenko, [Chinatsu Aone](https://md.research.elamur.ai/free/authors/chinatsu-aone/), Jason Tibbetts · **Year:** 2004 · **Venue:** Proceedings of the Conference on Reference Resolution and Its Applications · **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/W04-0704/) · [PDF](https://aclanthology.org/W04-0704.pdf) · [OpenAlex](https://openalex.org/W2903480974) **Citations:** 17 citations (openalex-snapshot · 2026-10-14) · [11](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This paper addresses coreference resolution as a component of information extraction (IE). The task is to decide which mentions in a document refer to the same real-world entity, so that extracted facts can be merged per entity rather than per mention. The authors compare several ways of framing and solving the problem. They propose a loss-based decoding framework, in which grouping mentions into entities is treated as finding a partition that minimizes a loss built from pairwise decisions. Because exact decoding is expensive, they add a greedy algorithm that approximates it. Both are combined with two learners, the Perceptron and logistic regression, which score whether mention pairs corefer. The methods are tested on the Automatic Content Extraction (ACE) data. The work matters because it links coreference modeling to the needs of entity-centric extraction pipelines. It also gives a reusable design: learn pairwise scores, then decode them into a global entity partition. ## Key points - The setting is IE-oriented coreference, which groups entity mentions into entities, as in ACE-style entity detection and tracking. - The core contributions are a loss-based decoding formulation for coreference and a greedy approximate decoding algorithm. - Two learning algorithms are paired with the decoders: the Perceptron and logistic regression. - Experiments use the ACE (Automatic Content Extraction) dataset. - **Caveat for the library:** the supplied PDF text is almost entirely garbled by a font-encoding failure, so this summary rests on the paper's widely known framing. Exact scores, dataset sizes and table values could not be read and are omitted. Please re-extract the text (e.g., via OCR) before adding concrete numbers. *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 11 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 5 not yet. - [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://md.research.elamur.ai/free/papers/alkan-2026-do-lexical-contextual-coreference-resolution/) - [Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly](https://md.research.elamur.ai/free/papers/chen-2021-tackling-zero-pronoun-resolution-non/) - [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://md.research.elamur.ai/free/papers/hsu-2022-contrastive-representation-learning-cross-document/) - [ParCorFull: a Parallel Corpus Annotated with Full Coreference](https://md.research.elamur.ai/free/papers/koltunski-2018-parcorfull-parallel-corpus-annotated-full/) - [Machine Learning for Coreference Resolution: From Local Classification to Global Ranking](https://md.research.elamur.ai/free/papers/ng-2005-machine-learning-coreference-resolution-local/) - [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zelenko2004coreference, title = {{Coreference Resolution for Information Extraction}}, author = {Zelenko, Dmitry and Aone, Chinatsu and Tibbetts, Jason}, booktitle = {Proceedings of the Conference on Reference Resolution and Its Applications}, year = {2004}, url = {https://aclanthology.org/W04-0704/} } ``` - [← previous Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems](https://md.research.elamur.ai/free/papers/gabsdil-2004-combining-acoustic-pragmatic-features-predict/) - [next → Corpus Pattern Analysis](https://md.research.elamur.ai/free/papers/lex-2004-hanks-corpus-pattern-analysis/) - [↑ back to papers of 2004](https://md.research.elamur.ai/free/papers/2004/)