--- title: "A Twin-Candidate Model of Coreference Resolution — Yang 2005" description: "The twin-candidate approach to coreference resolution compares pairs of antecedent candidates and selects the preferred one." authors: ["Xiaofeng Yang", "Jian Su", "Chew Lim Tan"] year: 2005 venue: "Second International Joint Conference on Natural Language Processing: Full Papers" source: "https://aclanthology.org/I05-1063/" doi: "10.1007/11562214_63" 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/yang-2005-twin-candidate-model-coreference-resolution/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/yang-2005-twin-candidate-model-coreference-resolution/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2005](https://md.research.elamur.ai/free/papers/2005/) / A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability # A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability **Authors:** [Xiaofeng Yang](https://md.research.elamur.ai/free/authors/xiaofeng-yang/), [Jian Su](https://md.research.elamur.ai/free/authors/jian-su/), [Chew Lim Tan](https://md.research.elamur.ai/free/authors/chew-lim-tan/) · **Year:** 2005 · **Venue:** Second International Joint Conference on Natural Language Processing: Full Papers · **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/I05-1063/) · [PDF](https://aclanthology.org/I05-1063.pdf) · [DOI 10.1007/11562214\_63](https://doi.org/10.1007/11562214_63) · [OpenAlex](https://openalex.org/W2132409270) **Citations:** 12 citations (openalex-snapshot · 2026-10-14) · [5](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The twin-candidate approach to coreference resolution compares pairs of antecedent candidates and selects the preferred one. It is good at choosing antecedents, but it always returns some antecedent, even when the noun phrase is not anaphoric at all. Earlier systems handled this with a separate anaphoricity determination step or a confidence threshold. Yang, Su and Tan instead change how the model is trained. Non-anaphoric NPs are paired with a randomly chosen anchor candidate and every other candidate, and these training instances get a third label, "00", meaning that neither candidate is preferred. During round-robin resolution, a "00" verdict penalizes both candidates. If no candidate ends with a positive score, the NP is left unresolved. On the MUC-6 and MUC-7 newswire data, this approach (`TC_NEW`) beat a single-candidate baseline and both alternative twin-candidate setups. Its behaviour was also much less sensitive to threshold settings. The result matters because the twin-candidate model can now perform full coreference resolution without any extra module. ## Key points - **Labels:** Training instances have the form i{C1, C2, M}. The labels "10" and "01" express which candidate is preferred, and the new "00" label marks NPs that are not anaphors. - **Systems compared:** - `SC`: single-candidate baseline. - `TC_AD`: twin-candidate with an anaphoricity determination filter. The authors note such filters reach only about 80% accuracy. This system had high precision but low recall. - `TC_THRESH`: twin-candidate with a confidence threshold. Recall exceeded 70%, but precision was the lowest of all systems. - `TC_NEW`: the proposed framework. - **Main result:** `TC_NEW` reached an F-measure of 67.3% on MUC-6 and 67.2% on MUC-7. This was significantly better than the other twin-candidate systems (sign test, p ≤ 0.05), and its precision was the highest of the four systems. - **Training data:** The default setup used 30 training documents. With 100 additional annotated documents, `TC_NEW` kept improving and peaked at about 90 documents, while `SC` showed little change. - **Threshold sensitivity:** `TC_THRESH` gave acceptable results only with thresholds above 95; at lower values its precision stayed under 40%. In contrast, `TC_NEW` remained stable across thresholds from 65 to 100 and works well with no threshold at all. *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 5 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 3 not yet. - [Dependency-driven Anaphoricity Determination for Coreference Resolution](https://md.research.elamur.ai/free/papers/kong-2010-dependency-driven-anaphoricity-determination-coreference/) - [Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation](https://md.research.elamur.ai/free/papers/zhou-2009-global-learning-noun-phrase-anaphoricity/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{yang2005twin, title = {{A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability}}, author = {Yang, Xiaofeng and Su, Jian and Tan, Chew Lim}, booktitle = {Second International Joint Conference on Natural Language Processing: Full Papers}, year = {2005}, doi = {10.1007/11562214\_63}, url = {https://doi.org/10.1007/11562214_63} } ``` - [← previous A Hierarchical Phrase-Based Model for Statistical Machine Translation](https://md.research.elamur.ai/free/papers/chiang-2005-hierarchical-phrase-based-model-statistical/) - [next → Adaptation of the translation model for statistical machine translation based on information retrieval](https://md.research.elamur.ai/free/papers/hildebrand-2005-adaptation-translation-model-statistical-machine/) - [↑ back to papers of 2005](https://md.research.elamur.ai/free/papers/2005/) ## References 11 references, 5 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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