---
title: "An Entity-Mention Model for Coreference — Yang 2008"
description: "Most learning-based coreference systems use the mention-pair model, which classifies two mentions at a time."
authors: ["Xiaofeng Yang", "Jian Su", "Jun Lang", "Chew Lim Tan", "Ting Liu", "Sheng Li"]
year: 2008
venue: "Proceedings of ACL-08: HLT"
source: "https://aclanthology.org/P08-1096/"
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/yang-2008-entity-mention-model-coreference-resolution/"
---
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# An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming

**Authors:** [Xiaofeng Yang](https://md.research.elamur.ai/free/authors/xiaofeng-yang/), [Jian Su](https://md.research.elamur.ai/free/authors/jian-su/), Jun Lang, [Chew Lim Tan](https://md.research.elamur.ai/free/authors/chew-lim-tan/), [Ting Liu](https://md.research.elamur.ai/free/authors/ting-liu/), [Sheng Li](https://md.research.elamur.ai/free/authors/sheng-li/) · **Year:** 2008 · **Venue:** Proceedings of ACL-08: HLT · **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/P08-1096/) · [PDF](https://aclanthology.org/P08-1096.pdf) · [OpenAlex](https://openalex.org/W2113310984)

**Citations:** 65 citations (openalex-snapshot · 2026-10-14) · [29](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most learning-based coreference systems use the mention-pair model, which classifies two mentions at a time. This misses evidence spread across the rest of an entity: a pronoun alone, for example, reveals little beyond gender. Earlier entity-mention models addressed this with hand-crafted first-order features that summarize an entity, such as "Any-X" or "Most-X" aggregations. These features are ad hoc and cannot describe individual mentions inside the entity.

The authors recast entity-level coreference with Inductive Logic Programming (ILP). Entities, their member mentions and the active mention are encoded as relational facts, using a has_mention relation plus standard lexical, syntactic and agreement features. ILP then learns first-order rules that decide whether a mention should join a partially built entity. Resolution uses greedy clustering.

On the ACE data set (NWire, NPaper and BNews domains), the ILP entity-mention model outperforms:
- an ILP mention-pair baseline, with F-measure gains of up to 2.3%;
- an entity-mention model built on heuristic first-order features.

The work is presented as the first use of ILP for coreference. It shows that learned relational rules can exploit entity-wide information for this task.

## Key points
- Evaluation is on ACE across three domains: NWire, NPaper and BNews. The baselines are Soon et al. (2001)-style mention-pair systems trained with C4.5 or ILP.
- Against the ILP mention-pair model, the entity-mention model's F-measure rises by 2.3% (NWire), 0.4% (NPaper) and 1.4% (BNews). The NWire and BNews gains are statistically significant (2-tailed t test, p \< 0.05).
- The gains come from precision, which rises by up to 4.6% (BNews). Recall falls by at most 1.8%.
- The ILP model also beats the entity-mention model with manually designed "Any-X" features by 1.0–1.8% F-measure.
- Adding all antecedents of an entity as training pairs in the mention-pair model hurts it: precision drops by up to 12%. Learned rules, by contrast, can refer to several mentions of an entity at once, for example a rule linking a pronoun to an entity that contains both a name and an indefinite NP in subject position.

*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

29 papers of our library cite it (of the 140673 whose bibliographies we read); 16 of them have a page here, 13 not yet.

- [Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features](https://md.research.elamur.ai/free/papers/bjorkelund-2014-learning-structured-perceptrons-coreference-resolution/)
- [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/papers/clark-2015-entity-centric-coreference-resolution-model/)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://md.research.elamur.ai/free/papers/clark-2016-improving-coreference-resolution-learning-entity/)
- [Decentralized Entity-Level Modeling for Coreference Resolution](https://md.research.elamur.ai/free/papers/durrett-2013-decentralized-entity-level-modeling-coreference/)
- [Latent Trees for Coreference Resolution](https://md.research.elamur.ai/free/papers/fernandes-2014-latent-trees-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/)
- [Unsupervised Ranking Model for Entity Coreference Resolution](https://md.research.elamur.ai/free/papers/ma-2016-unsupervised-ranking-model-entity-coreference/)
- [Latent Structures for Coreference Resolution](https://md.research.elamur.ai/free/papers/martschat-2015-latent-structures-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/)
- [Supervised Models for Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2009-supervised-models-coreference-resolution/)
- [Coreference Resolution with World Knowledge](https://md.research.elamur.ai/free/papers/rahman-2011-coreference-resolution-world-knowledge/)
- [Translation-Based Projection for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/rahman-2012-translation-based-projection-multilingual-coreference/)
- [A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2013-constraint-based-hypergraph-partitioning-approach/)
- [A Fully-Lexicalized Probabilistic Model for Japanese Zero Anaphora Resolution](https://md.research.elamur.ai/free/papers/sasano-2008-fully-lexicalized-probabilistic-model-japanese/)
- [A Discriminative Hierarchical Model for Fast Coreference at Large Scale](https://md.research.elamur.ai/free/papers/wick-2012-discriminative-hierarchical-model-fast-coreference/)
- [Global Learning of Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation](https://md.research.elamur.ai/free/papers/zhou-2009-global-learning-noun-phrase-anaphoricity/)

## Cite

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

```bibtex
@inproceedings{yang2008entity,
  title = {{An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming}},
  author = {Yang, Xiaofeng and Su, Jian and Lang, Jun and Tan, Chew Lim and Liu, Ting and Li, Sheng},
  booktitle = {Proceedings of ACL-08: HLT},
  year = {2008},
  url = {https://aclanthology.org/P08-1096/}
}
```

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