---
title: "Chinese Zero Pronoun Resolution — Chen 2014"
description: "The paper addresses anaphoric zero pronoun (AZP) resolution in Chinese, which means linking an omitted subject to an earlier noun phrase."
authors: ["Chen Chen", "Vincent Ng"]
year: 2014
venue: "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/D14-1084/"
doi: "10.3115/v1/d14-1084"
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/chen-2014-chinese-zero-pronoun-resolution-unsupervised/"
---
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# Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers

**Authors:** [Chen Chen](https://md.research.elamur.ai/free/en/authors/chen-chen/), [Vincent Ng](https://md.research.elamur.ai/free/en/authors/vincent-ng/) · **Year:** 2014 · **Venue:** Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **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/D14-1084/) · [PDF](https://aclanthology.org/D14-1084.pdf) · [DOI 10.3115/v1/d14-1084](https://doi.org/10.3115/v1/d14-1084) · [OpenAlex](https://openalex.org/W2251229550)

**Citations:** 11 citations (openalex-snapshot · 2026-10-14) · [6](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses anaphoric zero pronoun (AZP) resolution in Chinese, which means linking an omitted subject to an earlier noun phrase. Previous strong systems depended on supervised training with manually resolved zero pronouns. Chen and Ng propose an unsupervised generative model built on one idea: a resolver trained without labels on *overt* pronouns can be transferred to zero pronouns. Using EM, the model learns two things from raw text. The first is how ten Chinese personal pronouns relate to candidate antecedents through Number, Gender, Person and Animacy. The second is how contextual features predict coreference. At test time, AZPs are handled left to right, and each resolved gap is filled with its chosen antecedent so that later, more distant zero pronouns can draw on it. On the Chinese portion of OntoNotes 5.0 with gold AZPs, the model matches the best supervised resolver. The authors also introduce a rule-based AZP identifier for end-to-end evaluation. This matters because the generative process is not language-specific, so the approach could serve languages that lack annotated zero-pronoun data.

## Key points
- The model is trained only on overt pronouns that are surface or deep subjects, because 99.56% of zero pronouns in OntoNotes 5.0 occupy subject positions. Candidate antecedents are maximal or modifier NPs no more than two sentences back.
- With gold AZPs (Setting 1), the model reaches an F-score of 47.7 (R 47.5, P 47.9), equal to the supervised state of the art (Chen and Ng, 2013). In the hardest setting (Setting 3), F falls to 15.7.
- Removing the context features costs 14.7 F points in Setting 1. The rule-based context feature (feature 8) is the most valuable single feature, at −6.4 F. Dropping Number changes nothing, since nearly all candidates are singular.
- An analysis of 100 errors identifies three main causes:
  - missed document topics (32 errors);
  - weak semantic-compatibility modeling based only on mutual information with the governing verb (28 errors);
  - failure to exploit semantically parallel sentences (23 errors).

*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

6 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 1 not yet.

- [Anaphoric Zero Pronoun Identification: A Multilingual Approach](https://md.research.elamur.ai/free/en/papers/aloraini-2020-anaphoric-zero-pronoun-identification-multilingual/)
- [Cross-lingual Zero Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/aloraini-2020-cross-lingual-zero-pronoun-resolution/)
- [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/)
- [Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers](https://md.research.elamur.ai/free/en/papers/chen-2015-chinese-zero-pronoun-resolution-joint/)

## Cite

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

```bibtex
@inproceedings{chen2014chinese,
  title = {{Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers}},
  author = {Chen, Chen and Ng, Vincent},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1084},
  url = {https://doi.org/10.3115/v1/d14-1084}
}
```

- [← previous Chinese Irony Corpus Construction and Ironic Structure Analysis](https://md.research.elamur.ai/free/en/papers/tang-2014-chinese-irony-corpus-construction-ironic/)
- [next → CIDEr: Consensus-based Image Description Evaluation](https://md.research.elamur.ai/free/en/papers/vedantam-2014-cider-consensus-based-image-description/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/)

## References

27 references, 18 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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