---
title: "Multi-Lingual Coreference Resolution With — Luo 2005"
description: "The paper examines whether structural cues from automatically generated parse trees can improve statistical coreference resolution in several languages."
authors: ["Xiaoqiang Luo", "Imed Zitouni"]
year: 2005
venue: "Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/H05-1083/"
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/luo-2005-multi-lingual-coreference-resolution-syntactic/"
---
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# Multi-Lingual Coreference Resolution With Syntactic Features

**Authors:** [Xiaoqiang Luo](https://md.research.elamur.ai/free/authors/xiaoqiang-luo/), [Imed Zitouni](https://md.research.elamur.ai/free/authors/imed-zitouni/) · **Year:** 2005 · **Venue:** Proceedings of Human Language Technology Conference and 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/H05-1083/) · [PDF](https://aclanthology.org/H05-1083.pdf) · [OpenAlex](https://openalex.org/W2046731689)

**Citations:** 34 citations (openalex-snapshot · 2026-10-14) · [12](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper examines whether structural cues from automatically generated parse trees can improve statistical coreference resolution in several languages. Luo and Zitouni (IBM T.J. Watson) build on a maximum entropy entity-mention framework. In it, a link model scores attaching a mention to an existing entity, and a start model scores creating a new entity. Instead of writing rules and weighting them by hand, they convert parse-tree structure into trainable features, so the learner can assign low weight to signals coming from noisy parses.

Guided by binding theory, they approximate governing category indirectly. They use a c-command test plus counts of NPs, VPs and clauses along the c-command path. They also add dependency features (shared heads and head POS at one or two levels, direct modification) and apposition/same-parent features.

Experiments use ACE data for Arabic, Chinese and English. The features give statistically significant F-measure gains for Arabic and English but barely affect Chinese, which the authors link to properties such as pro-drop. The study shows that linguistically motivated features designed on English can carry over to other languages when learned from machine parses.

## Key points
- Binding-theory features: binary c-command (ccmd) between the minimal NPs of a candidate and a pronoun, plus NPcount, VPcount and Scount along the c-command path. These implicitly encode governing category and clause boundaries.
- Parsers are statistical models trained on treebanks: the Penn Treebank (English), the Chinese treebank of Xia et al. (2000), and the Arabic treebank of Maamouri and Bies (2004).
- ACE 2004 blind test, F-measure (baseline → +synt):
  - Arabic: 80.1 → 81.5
  - Chinese: 84.7 → 84.7
  - English: 80.6 → 82.0
  - Test sets contain about 10–11K mentions per language.
- ACE-Value gains are smaller (Arabic 88.0 → 88.9, Chinese 92.7 → 92.8, English 90.9 → 91.6) because the metric heavily discounts pronouns. For this reason, contrastive results are reported with F-measure.
- The improvement is concentrated on targeted mention types: English pronoun F-measure rises from 69.5% to 73.7% on the ACE 2004 test set.

*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

12 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 6 not yet.

- [Understanding the Value of Features for Coreference Resolution](https://md.research.elamur.ai/free/papers/bengtson-2008-understanding-value-features-coreference-resolution/)
- [Data-driven Multilingual Coreference Resolution using Resolver Stacking](https://md.research.elamur.ai/free/papers/bjorkelund-2012-data-driven-multilingual-coreference-resolution/)
- [Supervised Noun Phrase Coreference Research: The First Fifteen Years](https://md.research.elamur.ai/free/papers/ng-2010-supervised-noun-phrase-coreference-research/)
- [Creating a Coreference Resolution System for Italian](https://md.research.elamur.ai/free/papers/poesio-2010-creating-coreference-resolution-system-italian/)
- [Coreference Resolution across Corpora: Languages, Coding Schemes, and Preprocessing Information](https://md.research.elamur.ai/free/papers/recasens-2010-coreference-resolution-across-corpora-languages/)
- [Coreference Systems Based on Kernels Methods](https://md.research.elamur.ai/free/papers/versley-2008-coreference-systems-based-kernels-methods/)

## Cite

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

```bibtex
@inproceedings{luo2005multi,
  title = {{Multi-Lingual Coreference Resolution With Syntactic Features}},
  author = {Luo, Xiaoqiang and Zitouni, Imed},
  booktitle = {Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing},
  year = {2005},
  doi = {10.3115/1220575.1220658},
  url = {https://doi.org/10.3115/1220575.1220658}
}
```

- [← previous Morphological Analysis and Generation for Arabic Dialects](https://md.research.elamur.ai/free/papers/aclbulk-2005-habash-morphological-analysis-generation-arabic-dialects/)
- [next → Non-Projective Dependency Parsing using Spanning Tree Algorithms](https://md.research.elamur.ai/free/papers/narrative-2005-mcdonald-non-projective-dependency-parsing-spanning/)
- [↑ back to papers of 2005](https://md.research.elamur.ai/free/papers/2005/)

## References

25 references, 13 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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