--- title: "Joint Determination of Anaphoricity and — Denis 2007" description: "Pairwise coreference systems only decide anaphoricity implicitly." authors: ["Pascal Denis", "Jason Baldridge"] year: 2007 venue: "Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference" source: "https://aclanthology.org/N07-1030/" 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/denis-2007-joint-determination-anaphoricity-coreference-resolution/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/denis-2007-joint-determination-anaphoricity-coreference-resolution/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2007](https://md.research.elamur.ai/free/papers/2007/) / Joint Determination of Anaphoricity and Coreference Resolution using Integer Programming # Joint Determination of Anaphoricity and Coreference Resolution using Integer Programming **Authors:** [Pascal Denis](https://md.research.elamur.ai/free/authors/pascal-denis/), [Jason Baldridge](https://md.research.elamur.ai/free/authors/jason-baldridge/) · **Year:** 2007 · **Venue:** Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference · **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/N07-1030/) · [PDF](https://aclanthology.org/N07-1030.pdf) · [OpenAlex](https://openalex.org/W2130031580) **Citations:** 151 citations (openalex-snapshot · 2026-10-14) · [73](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Pairwise coreference systems only decide anaphoricity implicitly. As a result, they may link non-anaphoric mentions to antecedents or leave genuine anaphors unresolved. Earlier fixes placed a separate anaphoricity classifier in front of the coreference model as a filter, but such cascades depend on a carefully tuned threshold. Denis and Baldridge instead treat coreference as an integer linear programming (ILP) problem. Two maximum entropy classifiers, one scoring mention pairs and one scoring whether a mention is anaphoric, supply probabilities that serve as weights in the ILP objective. Linear constraints then force the two sets of decisions to be consistent. The authors first build a coreference-only ILP (COREF-ILP) and then the joint model (JOINT-ILP). They test on the three genres of ACE Phase 2 (BNEWS, NPAPER, NWIRE), using true ACE mentions and the Vilain et al. (1995) metric. The joint model improves f-score by 3.7–5.3% over the pairwise baseline, and all differences are significant. The result matters because global inference can combine independently trained local models quickly and without threshold tuning. ## Key points - Base models are maximum entropy classifiers trained with TADM (Gaussian prior, variance 1000). Training pairs follow Soon et al. (2001), and preprocessing uses the OpenNLP Toolkit. - Baseline f-scores for COREF-PAIRWISE are 63.9 on BNEWS, 67.6 on NPAPER and 63.8 on NWIRE. The filter cascade (AC-CASCADE) scores lower on BNEWS and NPAPER and only marginally higher on NWIRE. - The anaphoricity classifier reaches about 80.2% average accuracy. Even with perfect linkage (ORACLE-LINK), its errors cap a cascade at roughly 80–83 f-score. - JOINT-ILP yields f-score gains of 5.3%, 4.9% and 3.7% across the three ACE datasets, with significance tested by paired t-tests (p \< .05). - Planned future work includes transitivity constraints, partition-level constraints and linguistic constraints such as pronoun restrictions. *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 73 papers of our library cite it (of the 140673 whose bibliographies we read); 41 of them have a page here, 32 not yet. - [Understanding the Value of Features for Coreference Resolution](https://md.research.elamur.ai/free/papers/bengtson-2008-understanding-value-features-coreference-resolution/) - [Evaluation Metrics For End-to-End Coreference Resolution Systems](https://md.research.elamur.ai/free/papers/cai-2010-evaluation-metrics-end-end-coreference/) - [A Constrained Latent Variable Model for Coreference Resolution](https://md.research.elamur.ai/free/papers/chang-2013-constrained-latent-variable-model-coreference/) - [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/) - 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BibTeX from the library's record of the work. ```bibtex @inproceedings{denis2007joint, title = {{Joint Determination of Anaphoricity and Coreference Resolution using Integer Programming}}, author = {Denis, Pascal and Baldridge, Jason}, booktitle = {Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference}, year = {2007}, url = {https://aclanthology.org/N07-1030/} } ``` - [← previous Improving Translation Quality by Discarding Most of the Phrasetable](https://md.research.elamur.ai/free/papers/johnson-2007-improving-translation-quality-discarding-most/) - [next → Large Language Models in Machine Translation](https://md.research.elamur.ai/free/papers/brants-2007-large-language-models-machine-translation/) - [↑ back to papers of 2007](https://md.research.elamur.ai/free/papers/2007/) ## References 18 references, 10 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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