---
title: "Entity-Centric Coreference Resolution with — Clark 2015"
description: "Coreference systems that score pairs of mentions are accurate and simple to train, but they ignore what is already known about whole entities."
authors: ["Kevin Clark", "Christopher D. Manning"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/P15-1136/"
doi: "10.3115/v1/p15-1136"
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/en/papers/clark-2015-entity-centric-coreference-resolution-model/"
---
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# Entity-Centric Coreference Resolution with Model Stacking

**Authors:** [Kevin Clark](https://md.research.elamur.ai/free/en/authors/kevin-clark/), [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long 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/P15-1136/) · [PDF](https://aclanthology.org/P15-1136.pdf) · [DOI 10.3115/v1/p15-1136](https://doi.org/10.3115/v1/p15-1136) · [OpenAlex](https://openalex.org/W2252031764)

**Citations:** 159 citations (openalex-snapshot · 2026-10-14) · [93](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Coreference systems that score pairs of mentions are accurate and simple to train, but they ignore what is already known about whole entities. They are usually combined with heuristics such as best-first clustering, which can yield incoherent chains like [Hillary Clinton, Clinton, he]. Clark and Manning bridge pairwise and entity-centric approaches through model stacking. They first train two logistic mention pair models: a classification model and a ranking model. The outputs of these models then serve as cluster-level features for an agent that merges clusters agglomeratively. The agent works in easy-first order and is trained by imitation learning based on DAgger. Rather than relying on heuristic notions of good actions, each merge receives an exact cost derived from coreference evaluation metrics, which captures how severe a mistake is. The pairwise scores also order candidate merges and prune unlikely ones, which makes training tractable. On the English part of the CoNLL 2012 Shared Task, the system reaches a CoNLL average F1 of 63.02 and surpasses earlier state-of-the-art systems. This shows that learned, metric-driven clustering over stacked pairwise scores is effective.

## Key points
- The two mention pair models differ only in their training objective:
  - The classification model is optimized over all candidate antecedents.
  - The ranking model uses log-likelihood over the best true and best false antecedent, which keeps scores comparable across mentions.
  - Both use L1 regularization (λ = 0.001) and are optimized with AdaGrad.
- Features include distance, syntactic, semantic, lexical and rule-based cues, plus a conjunction scheme following Durrett and Klein (2013).
- Pairwise scores sort merges in easy-first order, and a threshold discards more than 95% of mention pairs before the agent runs.
- Test-set results on CoNLL 2012 English:
  - This system: 63.02 CoNLL F1.
  - Durrett & Klein (JOINT): 61.71.
  - Björkelund & Kuhn: 61.63.
  - Ma et al.: 61.56.
  - The system has the best B³ F1 (60.44) and CEAFφ4 F1 (56.02).
- The learned entity-centric agent clearly beats best-first clustering. Combining both pairwise models improves accuracy, while adding classic entity-level features did not help.

*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/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

93 papers of our library cite it (of the 140673 whose bibliographies we read); 44 of them have a page here, 49 not yet.

- [Anaphora Resolution for Twitter Conversations: An Exploratory Study](https://md.research.elamur.ai/free/en/papers/akta-2018-anaphora-resolution-twitter-conversations-exploratory/)
- [Neural Coreference Resolution for Arabic](https://md.research.elamur.ai/free/en/papers/aloraini-2020-neural-coreference-resolution-arabic/)
- [Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/)
- [Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://md.research.elamur.ai/free/en/papers/choubey-2017-event-coreference-resolution-iteratively-unfolding/)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://md.research.elamur.ai/free/en/papers/clark-2016-deep-reinforcement-learning-mention-ranking/)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://md.research.elamur.ai/free/en/papers/clark-2016-improving-coreference-resolution-learning-entity/)
- [Do UD Trees Match Mention Spans in Coreference Annotations?](https://md.research.elamur.ai/free/en/papers/coref-2021-popel-do-ud-trees-match-mention/)
- [Neural Models for Reasoning over Multiple Mentions Using Coreference](https://md.research.elamur.ai/free/en/papers/dhingra-2018-neural-models-reasoning-over-multiple/)
- [The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/emami-2019-knowref-coreference-corpus-removing-gender/)
- [ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain](https://md.research.elamur.ai/free/en/papers/fang-2021-chemu-ref-corpus-modeling-anaphora/)
- [What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/en/papers/fang-2022-what-does-it-take-bake/)
- [Coreference in Wikipedia: Main Concept Resolution](https://md.research.elamur.ai/free/en/papers/ghaddar-2016-coreference-wikipedia-main-concept-resolution/)
- [NARC – Norwegian Anaphora Resolution Corpus](https://md.research.elamur.ai/free/en/papers/hlum-2022-narc-norwegian-anaphora-resolution-corpus/)
- [BERT for Coreference Resolution: Baselines and Analysis](https://md.research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
- [Coreference Resolution with Entity Equalization](https://md.research.elamur.ai/free/en/papers/kantor-2019-coreference-resolution-entity-equalization/)
- [Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking](https://md.research.elamur.ai/free/en/papers/kundu-2018-neural-cross-lingual-coreference-resolution/)
- [Discourse Coherence in the Wild: A Dataset, Evaluation and Methods](https://md.research.elamur.ai/free/en/papers/lai-2018-discourse-coherence-wild-dataset-evaluation/)
- [The Referential Reader: A Recurrent Entity Network for Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/liu-2019-referential-reader-recurrent-entity-network/)
- [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ma-2016-unsupervised-ranking-model-entity-coreference/)
- [A Mention-Ranking Model for Abstract Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/marasovic-2017-mention-ranking-model-abstract-anaphora/)
- [Search Space Pruning: A Simple Solution for Better Coreference Resolvers](https://md.research.elamur.ai/free/en/papers/moosavi-2016-search-space-pruning-simple-solution/)
- [Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric](https://md.research.elamur.ai/free/en/papers/moosavi-2016-which-coreference-evaluation-metric-do/)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/)
- [A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation](https://md.research.elamur.ai/free/en/papers/poesio-2019-crowdsourced-corpus-multiple-judgments-disagreement/)
- [IMS HotCoref DE: A Data-driven Co-reference Resolver for German](https://md.research.elamur.ai/free/en/papers/roesiger-2016-ims-hotcoref-de-data-driven/)
- [Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis](https://md.research.elamur.ai/free/en/papers/shibata-2018-entity-centric-joint-modeling-japanese/)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://md.research.elamur.ai/free/en/papers/toshniwal-2020-learning-ignore-long-document-coreference/)
- [The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors](https://md.research.elamur.ai/free/en/papers/vala-2016-more-antecedents-merrier-resolving-multi/)
- [Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://md.research.elamur.ai/free/en/papers/webster-2018-mind-gap-balanced-corpus-gendered/)
- [Learning Global Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-coreference-resolution/)
- [CorefQA: Coreference Resolution as Query-based Span Prediction](https://md.research.elamur.ai/free/en/papers/wu-2020-corefqa-coreference-resolution-as-query/)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/xu-2020-revealing-myth-higher-order-inference/)
- [GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection](https://md.research.elamur.ai/free/en/papers/yu-2019-gumdrop-disrpt2019-shared-task-model/)
- [What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues](https://md.research.elamur.ai/free/en/papers/yu-2019-what-you-see-is-what/)
- [A Cluster Ranking Model for Full Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/yu-2020-cluster-ranking-model-full-anaphora/)
- [Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/yu-2020-free-plural-unrestricted-split-antecedent/)
- [AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference](https://md.research.elamur.ai/free/en/papers/yuan-2023-ambicoref-evaluating-human-model-sensitivity/)
- [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://md.research.elamur.ai/free/en/papers/zhang-2018-neural-coreference-resolution-deep-biaffine/)
- [Incorporating Context and External Knowledge for Pronoun Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zhang-2019-incorporating-context-external-knowledge-pronoun/)
- [Knowledge-aware Pronoun Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zhang-2019-knowledge-aware-pronoun-coreference-resolution/)
- [PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English](https://md.research.elamur.ai/free/en/papers/zhao-2022-pcr4all-comprehensive-evaluation-benchmark-pronoun/)
- [Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://md.research.elamur.ai/free/en/papers/zhu-2021-anatomy-ontogum-adapting-gum-ontonotes/)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://md.research.elamur.ai/free/en/papers/zhu-2021-ontogum-evaluating-contextualized-sota-coreference/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{clark2015entity,
  title = {{Entity-Centric Coreference Resolution with Model Stacking}},
  author = {Clark, Kevin and Manning, Christopher D.},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1136},
  url = {https://doi.org/10.3115/v1/p15-1136}
}
```

- [← previous Enhancing function word translation with syntax-based statistical post-editing](https://md.research.elamur.ai/free/en/papers/richardson-2015-enhancing-function-word-translation-syntax/)
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- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/)

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