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# Lexical Features in Coreference Resolution: To be Used With Caution

**Authors:** [Nafise Sadat Moosavi](https://md.mova.elamur.ai/free/en/docs/papers/authors/nafise-sadat-moosavi/), [Michael Strube](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-strube/) · **Year:** 2017 · **Venue:** ACL 2017 (short papers) **Link:** [aclanthology.org/P17-2003](https://aclanthology.org/P17-2003/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**31** citations](https://openalex.org/works?filter=cites:W2609608256) openalex-snapshot · 2026-10-14[**23** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/moosavi-2017-lexical-features-coreference-resolution-be/) of the 140673 papers whose bibliographies we read[Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/P17-2/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/P17-2003/)
- [PDF](https://aclanthology.org/P17-2003.pdf)
- [DOI 10.18653/v1/p17-2003](https://doi.org/10.18653/v1/p17-2003)
- [arXiv 1704.06779](https://arxiv.org/abs/1704.06779)
- [OpenAlex](https://openalex.org/W2609608256)

**Authors (2)**

1. [Nafise Sadat Moosavi](https://openalex.org/A5054918343) [ORCID](https://orcid.org/0000-0002-8332-307X) · [Semantic Scholar](https://www.semanticscholar.org/author/2182290) · h 12 · 673 citations · 90 works
2. [Michael Strube](https://openalex.org/A5020347604) [ORCID](https://orcid.org/0000-0002-4731-8142) · [Semantic Scholar](https://www.semanticscholar.org/author/31380436) · h 74 · 20622 citations · 483 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Learned coreference resolvers owe much of their gain over rule systems to lexical features (surface words or embeddings of heads, first/last words, context). The paper shows the cost. On WikiCoref, annotated with the same guidelines as CoNLL-2012, the Stanford rule-based sieve system (CoNLL F1 about 51.0) is on par with or ahead of berkeley, cort and deep-coref (51.0 down to 49.9, 52.7 for the best deep-coref variant), although on the CoNLL test set the learned systems are 6–10 points ahead. The reason is overlap: a large share of the coreferent mention-head pairs in the CoNLL test set already occur, as coreferent pairs, in training (38–51% by genre, 76–77% for the pivot/Bible genre), so lexical features memorise rather than generalise. The authors call for out-of-domain evaluation and features that capture linguistic properties rather than words.

## Ideas

- I1. Keep this as the argument for Mova's rule-first coreference, with numbers to defend it: report CoNLL F1 per GUM genre (bio, academic, conversation, fiction, …) on dev, not only the total, since `coref` was tuned on dev overall. A rule that helps one genre and hurts another shows up there.
- I2. Compute the paper's overlap diagnostic for Mova's own tuning: the share of coreferent head pairs in GUM test that also occur as coreferent pairs in GUM dev. If it is high, dev-tuned string sieves (`exact`, `strict_*`) are flattered on test, and the frozen-code test result in [coref — SLM coreference: a sieve of rules with a reason for every link](https://md.mova.elamur.ai/free/en/docs/coref/) should be read with that number beside it.
- I3. When a learned ranker is added (see `lee-2017-end-end-neural-coreference-resolution` I2), start from non-lexical features (agreement, syntactic role, distance, Hobbs rank, speaker, mention type) and add head words only if a cross-genre check (train on some genres, test on others, or test on tales) shows no loss.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §8–9. Code: `coref/src/eval.rs` (per-genre and overlap reports), `coref/src/sieve.rs`. Measures: CoNLL F1 per GUM genre on dev; head-pair overlap between dev and test.

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 10.18653/v1/p17-2003](https://doi.org/10.18653/v1/p17-2003) · [arXiv 1704.06779](https://arxiv.org/abs/1704.06779)

```
@inproceedings{moosavi2017lexical,
  title = {{Lexical Features in Coreference Resolution: To be Used With Caution}},
  author = {Moosavi, Nafise Sadat and Strube, Michael},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-2003},
  eprint = {1704.06779},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p17-2003}
}
```

- [← previous Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2016-which-coreference-evaluation-metric-do/)
- [next → Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2018-linguistic-features-improve-generalization-capability/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

15 references, 9 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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7. Nafise Sadat Moosavi and Michael Strube. 2016. Which coreference evaluation metric do you trust? A proposal for a link-based entity aware metric. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, 7–12 August 2016. pages 632–642. http://www.aclweb.org/anthology/P16-1060.pdf. [↗](https://aclanthology.org/P16-1060/) [our card](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2016-which-coreference-evaluation-metric-do/)
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