---
title: "Latent Trees for Coreference Resolution — Fernandes 2014"
description: "The paper addresses unrestricted coreference resolution: finding which mentions in a document point to the same entity, and keeping only the clusters…"
authors: ["Eraldo Rezende Fernandes", "Cícero Nogueira dos Santos", "Ruy Luiz Milidiú"]
year: 2014
venue: "Computational Linguistics"
source: "https://aclanthology.org/J14-4004/"
doi: "10.1162/coli_a_00200"
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/fernandes-2014-latent-trees-coreference-resolution/"
---
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# Latent Trees for Coreference Resolution

**Authors:** Eraldo Rezende Fernandes, [Cícero Nogueira dos Santos](https://md.research.elamur.ai/free/en/authors/cicero-nogueira-dos-santos/), Ruy Luiz Milidiú · **Year:** 2014 · **Venue:** Computational Linguistics · **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/J14-4004/) · [PDF](https://aclanthology.org/J14-4004.pdf) · [DOI 10.1162/coli\_a\_00200](https://doi.org/10.1162/coli_a_00200) · [OpenAlex](https://openalex.org/W2164632894)

**Citations:** 44 citations (openalex-snapshot · 2026-10-14) · [18](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses unrestricted coreference resolution: finding which mentions in a document point to the same entity, and keeping only the clusters with more than one mention. The authors treat the mentions of a document as nodes in a graph, with directed arcs between candidate coreferent pairs. Clustering then becomes the task of finding a set of trees in this graph. Because the gold trees are not annotated, they are treated as hidden structures. A large-margin latent structured perceptron learns them, and the Chu-Liu-Edmonds optimal branching algorithm decodes them. Entropy-guided feature induction automatically combines basic arc features into richer conjunctions, which lets a linear learner behave like a nonlinear model. The same system runs on the Arabic, Chinese and English CoNLL-2012 Shared Task data, with only small language-specific changes such as pronoun lists. It won the closed track. A later version adds nested-mention candidate arcs for Chinese and reaches the best reported results for all three languages. The work shows that latent trees keep structured coreference learning tractable without sacrificing accuracy.

## Key points
- **Pipeline:** rule-based mention detection (adapted from dos Santos and Carvalho 2011) → candidate arcs from sieves (adapted from Lee et al. 2013) → basic and EFI-induced features → latent tree learning and prediction → clusters read directly from the trees. Verbs are not used as mentions, so event coreference is not handled.
- **Metric:** the CoNLL-2012 score, i.e. the unweighted average of MUC, B³ and CEAF_e, further averaged over the three languages.
- **Shared Task results:** the submitted system scored 54.22 (Arabic), 58.49 (Chinese) and 63.37 (English), for an official score of 58.69, the best among participants.
- **Improved version:** adding nested-mention arcs for Chinese raises that language's score by almost 4.5 points. The overall score rises to 60.15 (+1.46 absolute, a 3.5% error reduction).
- **Error analysis:** wrongly attaching a singleton to a tree accounts for 45% of errors in Arabic, 37.7% in Chinese and 31.9% in English. Pleonastic "it" is a notable source of these errors in English. The authors propose higher-order and cluster-sensitive features as future work.

*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

18 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 13 not yet.

- [Neural Coreference Resolution for Arabic](https://md.research.elamur.ai/free/en/papers/aloraini-2020-neural-coreference-resolution-arabic/)
- [Latent Structures for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/martschat-2015-latent-structures-coreference-resolution/)
- [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/)
- [A Cluster Ranking Model for Full Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/yu-2020-cluster-ranking-model-full-anaphora/)
- [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/)

## Cite

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

```bibtex
@article{fernandes2014latent,
  title = {{Latent Trees for Coreference Resolution}},
  author = {Fernandes, Eraldo Rezende and Santos, Cícero Nogueira dos and Milidiú, Ruy Luiz},
  journal = {Computational Linguistics},
  year = {2014},
  doi = {10.1162/coli\_a\_00200},
  url = {https://doi.org/10.1162/coli_a_00200}
}
```

- [← previous Large Scale Arabic Error Annotation: Guidelines and Framework](https://md.research.elamur.ai/free/en/papers/zaghouani-2014-large-scale-arabic-error-annotation/)
- [next → 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/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/)

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66 references, 44 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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