---
title: "Bootstrapping coreference resolution using — Kobdani 2011"
description: "Most coreference resolution (CoRe) systems need human-annotated training data, so moving them to new domains, genres or languages is costly."
authors: ["Hamidreza Kobdani", "Hinrich Schuetze", "Michael Schiehlen", "Hans Kamp"]
year: 2011
venue: "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/P11-1079/"
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/kobdani-2011-bootstrapping-coreference-resolution-word-associations/"
---
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# Bootstrapping coreference resolution using word associations

**Authors:** [Hamidreza Kobdani](https://md.research.elamur.ai/free/authors/hamidreza-kobdani/), [Hinrich Schuetze](https://md.research.elamur.ai/free/authors/hinrich-schuetze/), Michael Schiehlen, Hans Kamp · **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/P11-1079/) · [PDF](https://aclanthology.org/P11-1079.pdf) · [OpenAlex](https://openalex.org/W2103651919)

**Citations:** 23 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most coreference resolution (CoRe) systems need human-annotated training data, so moving them to new domains, genres or languages is costly. Kobdani et al. propose a fully unsupervised alternative. Its central idea is that statistical associations between words, such as Obama and President, signal probable coreference. Such associations are too sparse to learn from small annotated corpora, but large unlabeled text supplies enough evidence.

The authors compute mutual information between markable heads over Wikipedia articles. The A-INF component then clusters markables using these scores together with a few simple hard filters. In the self-training subsystem UNSEL, A-INF's automatic labels become training data for a decision-tree-based supervised learner (SUCRE) that uses a rich feature space. This setup helps both precision and recall.

UNSEL beats earlier unsupervised systems on ACE-2. On SemEval-2010 OntoNotes, it nearly matches its supervised twin, which is trained identically except for using manual labels. This suggests unlabeled data can largely replace annotation.

## Key points
- **MCORE** is a modular framework with three modes:
  - A-INF: unsupervised, association scores only.
  - SUCRE: supervised.
  - UNSEL: self-trained, with A-INF labeling data for SUCRE.
- **Filters and search:** the system uses a small set of filters covering reflexives, pronoun distance, and number/gender agreement. Antecedents are searched within a 10-sentence window, since about 98.68% of antecedents fall inside it.
- **Learning curve:** as more Wikipedia articles are used, A-INF's recall rises and its precision falls, but F1 keeps improving up to tens of thousands of articles.
- **SemEval-2010 OntoNotes results (F1):**

  | Setting | System | MUC | B³ | CEAF |
  |---|---|---|---|---|
  | Gold markables | UNSEL | 63.0 | 86.9 | 79.7 |
  | Gold markables | SUCRE | 64.3 | 87.0 | 80.1 |
  | System markables | UNSEL | 55.0 | 69.8 | 66.3 |

  Both MCORE models outperform Relax, Tanl-1 and SUCRE 2010.
- **ACE-2 vs. OntoNotes gap:** UNSEL trails SUCRE clearly on ACE-2. The authors partly attribute this to OntoNotes having more singletons (85.2% vs 60.2%) and a roughly four times larger training 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

13 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 8 not yet.

- [Coreference Semantics from Web Features](https://md.research.elamur.ai/free/papers/bansal-2012-coreference-semantics-web-features/)
- [Active Learning for Coreference Resolution](https://md.research.elamur.ai/free/papers/laws-2012-active-learning-coreference-resolution/)
- [Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules](https://md.research.elamur.ai/free/papers/lee-2013-deterministic-coreference-resolution-based-entity/)
- [Solving Hard Coreference Problems](https://md.research.elamur.ai/free/papers/peng-2015-solving-hard-coreference-problems/)
- [Translation-Based Projection for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2012-translation-based-projection-multilingual-coreference/)

## Cite

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

```bibtex
@inproceedings{kobdani2011bootstrapping,
  title = {{Bootstrapping coreference resolution using word associations}},
  author = {Kobdani, Hamidreza and Schuetze, Hinrich and Schiehlen, Michael and Kamp, Hans},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1079/}
}
```

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## References

28 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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