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# A Machine Learning Approach to Coreference Resolution of Noun Phrases

**Authors:** Wee Meng Soon, [Hwee Tou Ng](https://md.mova.elamur.ai/free/en/docs/papers/authors/hwee-tou-ng/), Daniel Chung Yong Lim · **Year:** 2001 · **Venue:** Computational Linguistics 27(4) **Link:** [aclanthology.org/J01-4004](https://aclanthology.org/J01-4004/) (DOI [10.1162/089120101753342653](https://doi.org/10.1162/089120101753342653)) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**982** citations](https://openalex.org/works?filter=cites:W2164455818) openalex · 2026-10-18[**304** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/soon-2001-machine-learning-approach-coreference-resolution/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J01-4/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J01-4004/)
- [PDF](https://aclanthology.org/J01-4004.pdf)
- [DOI 10.1162/089120101753342653](https://doi.org/10.1162/089120101753342653)
- [OpenAlex](https://openalex.org/W2164455818)

**Authors (3)**

1. [Wee Meng Soon](https://openalex.org/A5086320526) h 3 · 999 citations · 4 works
2. [Hwee Tou Ng](https://openalex.org/A5110081955) h 63 · 14801 citations · 300 works
3. [Daniel Chung Yong Lim](https://openalex.org/A5061784033) h 2 · 987 citations · 3 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper presents a learned, end-to-end coreference resolver for general noun phrases (not only pronouns), trained on a small annotated corpus. A pipeline of HMM-based modules first finds the markables: tokenisation, POS tagging, NP identification, named entities and nested NPs. Each pair of markables is described by 12 shallow features: distance, pronoun flags, string match, definiteness, demonstratives, number, semantic class, gender, both-proper-names, alias, and apposition. A C5 decision tree classifies pairs, and closest-first clustering builds the chains. On MUC-6 the system reaches recall 58.6%, precision 67.3% and F 62.6. On MUC-7 it reaches recall 56.1%, precision 65.5% and F 60.4. Only 6.5% of the 20,910 MUC-6 training pairs are positive. The three features alias, string match and apposition alone come within 2.3 F points (MUC-6) and 1 point (MUC-7) of the full system.

## Ideas

- I1. Run a feature ablation in the way the paper's tables do: remove each of the 12 features in turn on the same split, and report the F change. A feature that moves nothing is a candidate for removal before any new feature is added (`coref/src/eval.rs`).
- I2. Audit the error classes of section 5 (modifier string match, same string but different referents, apposition errors, alias errors) as counts per class on a dev set. Each class has a rule that can be tested, so this works as a negative-control report.
- I3. Replace the learned tree with a deterministic decision list. The tree in the paper's Figure 2 is short enough to print, and each link can then carry the rule that produced it.
- I4. Keep "unknown" as a third value for gender and semantic agreement, as the paper does, so that missing knowledge is never read as disagreement.

## For Mova

The code is `coref/src/sieve.rs` (the pairwise features), `coref/src/mention.rs` (markables) and `coref/src/eval.rs`. The chapters are [35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) and, for number, [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/). The measure is MUC F on the same dev and test splits, with the ablation above. Run every configuration twice and check that the results match exactly.

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/089120101753342653](https://doi.org/10.1162/089120101753342653)

```
@article{soon2001machine,
  title = {{A Machine Learning Approach to Coreference Resolution of Noun Phrases}},
  author = {Soon, Wee Meng and Ng, Hwee Tou and Lim, Daniel Chung Yong},
  journal = {Computational Linguistics},
  year = {2001},
  doi = {10.1162/089120101753342653},
  url = {https://doi.org/10.1162/089120101753342653}
}
```

- [← previous A New English Grammar based on the recommendations of the Joint Committee on Grammatical Terminology, Parts I–III; The Soul of Grammar](https://md.mova.elamur.ai/free/en/docs/papers/sonnenschein-1916-new-english-grammar-based/)
- [next → Sentence Level Discourse Parsing using Syntactic and Lexical Information](https://md.mova.elamur.ai/free/en/docs/papers/soricut-2003-sentence-level-discourse-parsing-syntactic/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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