--- title: "Domain-specific vs. Uniform Modeling for — Uryupina 2012" description: "Coreference corpora such as ACE-02, ARRAU and OntoNotes combine several domains, but each domain is fairly small." authors: ["Olga Uryupina", "Massimo Poesio"] year: 2012 venue: "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)" source: "https://aclanthology.org/L12-1562/" 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/uryupina-2012-domain-specific-vs-uniform-modeling/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/uryupina-2012-domain-specific-vs-uniform-modeling/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2012](https://md.research.elamur.ai/free/papers/2012/) / Domain-specific vs. Uniform Modeling for Coreference Resolution # Domain-specific vs. Uniform Modeling for Coreference Resolution **Authors:** [Olga Uryupina](https://md.research.elamur.ai/free/authors/olga-uryupina/), [Massimo Poesio](https://md.research.elamur.ai/free/authors/massimo-poesio/) · **Year:** 2012 · **Venue:** Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12) · **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/L12-1562/) · [PDF](https://aclanthology.org/L12-1562.pdf) · [OpenAlex](https://openalex.org/W2251435592) **Citations:** 11 citations (openalex-snapshot · 2026-10-14) · [6](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Coreference corpora such as ACE-02, ARRAU and OntoNotes combine several domains, but each domain is fairly small. This raises a practical question: should a resolver be trained separately for each domain or on all the data at once? Uryupina and Poesio first introduce a simple measure of domain homogeneity. Every document is described by ten mention-distribution ratios, including the share of pronouns, names, singletons and the length of the longest chain. Homogeneity C dom is then the average distance of documents to their domain centroid. They then use the BART toolkit with J48 decision trees and system mentions to compare domain-specific and union training. Three feature sets are tested: the Soon et al. (2001) baseline, a rich set of 42 linguistic features, and a reduced, pre-selected half of that set. Domain choice barely affects the shallow baseline. The full rich set overfits, and it does so more on small domains. With feature selection, domain-specific models usually beat union models. This suggests that splitting by domain helps knowledge-rich resolvers when overfitting is controlled. ## Key points - **Corpora:** ARRAU (about 156K training tokens) covers news, medical, fiction and dialogue. ACE-02 (about 221K) and OntoNotes/CoNLL (about 1M) contain only news-type material. Because their annotation guidelines differ, cross-corpus training was ruled out. - **Domain structure:** The indicators separate ARRAU's domains cleanly (pear–rst centroid distance 0.65). ACE-02 subdomains overlap heavily. Some OntoNotes domains are near-identical (mz–wb 0.09). C dom values cannot be compared across corpora. - **Selected features (MUC F, domain vs. union):** ACE-02 scored 73.97 vs. 71.12 and OntoNotes 57.74 vs. 57.05, both significant. ARRAU stayed weaker (54.84 vs. 55.29). - **All 42 features:** Results dropped below the baseline for ARRAU and OntoNotes (e.g., OntoNotes about 49.5 vs. 54.2–54.5). Domain-specific training on small ACE domains made the drop worse. - **Small domains:** These should be merged with similar ones or trained on the union. For example, ARRAU vpc closely resembles rst. *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 6 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 4 not yet. - [Variation in Coreference Strategies across Genres and Production Media](https://md.research.elamur.ai/free/papers/akta-2020-variation-coreference-strategies-across-genres/) - [Anaphora Resolution with the ARRAU Corpus](https://md.research.elamur.ai/free/papers/poesio-2018-anaphora-resolution-arrau-corpus/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{uryupina2012domain, title = {{Domain-specific vs. Uniform Modeling for Coreference Resolution}}, author = {Uryupina, Olga and Poesio, Massimo}, booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, doi = {10.63317/5paidqcig542}, url = {https://doi.org/10.63317/5paidqcig542} } ``` - [← previous Domain Adaptation of Coreference Resolution for Radiology Reports](https://md.research.elamur.ai/free/papers/apostolova-2012-domain-adaptation-coreference-resolution-radiology/) - [next → Easy-first Coreference Resolution](https://md.research.elamur.ai/free/papers/stoyanov-2012-easy-first-coreference-resolution/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/papers/2012/) ## References 15 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Hal III Daume. 2007. Frustratingly easy domain adaptation. In Proc. [find](https://scholar.google.com/scholar?q=Hal+III+Daume%2E+2007%2E+Frustratingly+easy+domain+adaptation%2E+In+Proc%2E) 2. George Doddington, Alexis Mitchell, Mark Przybocki, Lance Ramshaw, Stephanie Strassell, and Ralph Weischedel. 2004. The automatic content extraction (ACE) program–tasks, data, and evaluation. In Proc. LREC-04. [find](https://scholar.google.com/scholar?q=George+Doddington%2C+Alexis+Mitchell%2C+Mark+Przybocki%2C+Lance+Ramshaw%2C+Stephanie+Strassell%2C+and+Ralph+Weischedel%2E+2004%2E+The+automatic+content+extraction+%28ACE%29+program%E2%80%93tasks%2C+data%2C+and+evaluation%2E+In+Proc%2E) [our card](https://md.research.elamur.ai/free/papers/doddington-2004-automatic-content-extraction-ace-program/) 3. Veronique Hoste. 2005. Optimization Issues in Machine Learning of Coreference Resolution. Ph.D. thesis, Antwerp University. 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