---
title: "A Pronoun Anaphora Resolution System based on — Li 2011"
description: "The paper addresses pronoun anaphora resolution, the task of linking a pronoun to the earlier mention it refers to, using a supervised generative…"
authors: ["Dingcheng Li", "Tim Miller", "William Schuler"]
year: 2011
venue: "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/P11-1117/"
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/li-2011-pronoun-anaphora-resolution-system-based/"
---
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# A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models

**Authors:** [Dingcheng Li](https://md.research.elamur.ai/free/authors/dingcheng-li/), [Tim Miller](https://md.research.elamur.ai/free/authors/tim-miller/), William Schuler · **Year:** 2011 · **Venue:** Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies · **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/P11-1117/) · [PDF](https://aclanthology.org/P11-1117.pdf) · [OpenAlex](https://openalex.org/W2166512823)

**Citations:** 15 citations (openalex-snapshot · 2026-10-14) · [5](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses pronoun anaphora resolution, the task of linking a pronoun to the earlier mention it refers to, using a supervised generative approach built on factorial hidden Markov models (FHMMs). The design is inspired by human incremental processing. The model reads text word by word and maintains a small hidden buffer of recently introduced entities, which serves as an explicit short-term discourse memory. Each hidden state has three coupled components: a referent operation (none, new or old mention), coreference features stored for each buffered entity, and a part-of-speech tag. As a result, POS tagging and pronoun resolution are decoded together. Pronouns are modeled as being generated by buffer entries, and noun phrases are represented by their head words. On the ACE corpus (BNEWS, NPAPER and NWIRE genres), the system achieved the highest average score among the compared systems, outperforming the mention-ranking model of Denis and Baldridge (2007) and the unsupervised emPronouns system. The authors argue that sequential models with an explicit entity memory are a promising alternative to pairwise and ranking approaches.

## Key points
- Each buffered mention carries four coreference features: index (recency order), named-entity type, number and gender. Gender and number distribution data came from Shane Bergsma.
- The FHMM system averaged 76.3, compared with 75.2 for the Denis and Baldridge ranking (RK) model, even when RK used a 10-sentence window. RK was 0.5 points better only on NWIRE.
- A paired Student's t-test against emPronouns showed statistically significant gains on all three genres (p \< 0.001). emPronouns was run with its original model files and was not retrained on ACE.
- In the training data, a mention distance of 14 covered every pronoun–antecedent pair. Very distant antecedents remain a source of errors because the buffer size is limited.
- Other errors came from plural candidates that could not be distinguished by gender, coordinate noun phrases that lack a single head word, and sparse or missing features. Sparsity was handled with add-one smoothing and deleted interpolation.

*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

5 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 3 not yet.

- [Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/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/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/)

## Cite

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

```bibtex
@inproceedings{li2011pronoun,
  title = {{A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models}},
  author = {Li, Dingcheng and Miller, Tim and Schuler, William},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1117/}
}
```

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## References

20 references, 10 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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