---
title: "A Constrained Latent Variable Model for — Chang 2013"
description: "Coreference resolution clusters noun-phrase mentions according to which real-world entity each one denotes."
authors: ["Kai-Wei Chang", "Rajhans Samdani", "Dan Roth"]
year: 2013
venue: "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D13-1057/"
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/chang-2013-constrained-latent-variable-model-coreference/"
---
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# A Constrained Latent Variable Model for Coreference Resolution

**Authors:** [Kai-Wei Chang](https://md.research.elamur.ai/free/en/authors/kai-wei-chang/), [Rajhans Samdani](https://md.research.elamur.ai/free/en/authors/rajhans-samdani/), [Dan Roth](https://md.research.elamur.ai/free/en/authors/dan-roth/) · **Year:** 2013 · **Venue:** Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · **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/D13-1057/) · [PDF](https://aclanthology.org/D13-1057.pdf) · [OpenAlex](https://openalex.org/W2250893320)

**Citations:** 53 citations (openalex-snapshot · 2026-10-14) · [27](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Coreference resolution clusters noun-phrase mentions according to which real-world entity each one denotes. Earlier systems either relied on pairwise mention scores trained separately from the clustering step, or used richer mention–cluster models that need heuristics to stay tractable. The authors introduce the Latent Left-Linking Model (L3M). In this structured prediction model, each mention attaches to its highest-scoring preceding antecedent, or to a dummy mention if none qualifies, and the clustering is the transitive closure of these links. Training and decoding are coupled through a latent structural SVM objective, which is optimized with fast stochastic gradient descent and uses a decomposable pairwise loss related to the Rand index. A probabilistic extension, PL3M, considers several left-links at once, controlled by a temperature-like parameter. The constrained variants CL3M and CPL3M add domain-knowledge constraints. On ACE 2004 and OntoNotes-5.0, the models outperform correlational clustering, latent spanning forests, IllinoisCoref and the Stanford rule-based system. This shows that a well-trained mention-pair model can match more complex entity-level approaches.

## Key points
- Datasets: ACE 2004 and OntoNotes-5.0. Results are reported with MUC, B3 (BCUB) and CEAF-e metrics and their average.
- CL3M uses five constraints: SameSpan, SameDetNom, SameProperName, ModifierMismatch and PropertyMismatch.
- On OntoNotes-5.0 with predicted mentions, the constraints raise average F1 from 62.30 (L3M) to 63.59 (CL3M), a gain of 1.29. Of that gain, 1.19 comes from the four must-link constraints, which help because L3M has higher precision (78.38% B3) than recall (67.96%).
- Considering multiple links (γ = 0.2 vs. γ = 0) adds about 0.7 points on ACE 2004. With noisy predicted mentions, γ = 0 worked best.
- In the reported comparison, correlational clustering with exact ILP inference beat the spanning forest approach. Constraints helped less on ACE because its mentions are shorter phrases.

*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

27 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 14 not yet.

- [Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features](https://md.research.elamur.ai/free/en/papers/bjorkelund-2014-learning-structured-perceptrons-coreference-resolution/)
- [Chinese Zero Pronoun Resolution with Deep Neural Networks](https://md.research.elamur.ai/free/en/papers/chen-2016-chinese-zero-pronoun-resolution-deep/)
- [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/en/papers/clark-2015-entity-centric-coreference-resolution-model/)
- [A Joint Model for Entity Analysis: Coreference, Typing, and Linking](https://md.research.elamur.ai/free/en/papers/durrett-2014-joint-model-entity-analysis-coreference/)
- [Coreference Resolution with Entity Equalization](https://md.research.elamur.ai/free/en/papers/kantor-2019-coreference-resolution-entity-equalization/)
- [Prune-and-Score: Learning for Greedy Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ma-2014-prune-score-learning-greedy-coreference/)
- [A Joint Framework for Coreference Resolution and Mention Head Detection](https://md.research.elamur.ai/free/en/papers/peng-2015-joint-framework-coreference-resolution-mention/)
- [Solving Hard Coreference Problems](https://md.research.elamur.ai/free/en/papers/peng-2015-solving-hard-coreference-problems/)
- [Improving Generalization in Coreference Resolution via Adversarial Training](https://md.research.elamur.ai/free/en/papers/subramanian-2019-improving-generalization-coreference-resolution-adversarial/)
- [Limited memory incremental coreference resolution](https://md.research.elamur.ai/free/en/papers/webster-2014-limited-memory-incremental-coreference-resolution/)
- [Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/)
- [Learning Global Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-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/)

## Cite

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

```bibtex
@inproceedings{chang2013constrained,
  title = {{A Constrained Latent Variable Model for Coreference Resolution}},
  author = {Chang, Kai-Wei and Samdani, Rajhans and Roth, Dan},
  booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
  year = {2013},
  doi = {10.18653/v1/d13-1057},
  url = {https://doi.org/10.18653/v1/d13-1057}
}
```

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