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# Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers

**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:** 2018 · **Venue:** EMNLP 2018 **Link:** [aclanthology.org/D18-1018](https://aclanthology.org/D18-1018/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**19** citations](https://openalex.org/works?filter=cites:W2894706950) openalex · 2026-10-18[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/moosavi-2018-linguistic-features-improve-generalization-capability/) of the 140673 papers whose bibliographies we read[Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/D18-1018/)
- [PDF](https://aclanthology.org/D18-1018.pdf)
- [DOI 10.18653/v1/d18-1018](https://doi.org/10.18653/v1/d18-1018)
- [arXiv 1708.00160](https://arxiv.org/abs/1708.00160)
- [OpenAlex](https://openalex.org/W2894706950)

**Authors (2)**

1. [Nafise Sadat Moosavi](https://openalex.org/A5054918343) [ORCID](https://orcid.org/0000-0002-8332-307X) · h 12 · 717 citations · 92 works
2. [Michael Strube](https://openalex.org/A5020347604) [ORCID](https://orcid.org/0000-0002-4731-8142) · h 74 · 20807 citations · 485 works

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

## What it is about

Neural coreference resolvers that win on CoNLL-2012 lose their ranking on WikiCoref, which uses the same guidelines. The authors add classic linguistic features to deep-coref (mention type and fine type, named-entity type, gender, number, animacy, dependency relation, POS; pairwise agreement, string match, compatibility of pre- and post-modifiers, salience-based discourse features combining distance and subject/object role). Added wholesale they help little. Selecting only the informative (feature, value) combinations helps a lot: their EPM algorithm stores mention-pair samples in a frequent-pattern tree and keeps patterns that pass a G² likelihood-ratio test (p < 0.01), add information over their sub-patterns, and are frequent enough. The model with these patterns gains in-domain and gives the best out-of-domain results reported at the time on WikiCoref and other sets, on par with a system built specifically for WikiCoref.

## Ideas

- I1. Use EPM-style mining to choose feature conjunctions for `prag`'s perceptron, which [prag — Mova pragmatics: LLM silver and a transparent SLM](https://md.mova.elamur.ai/free/en/docs/prag/) lists as a next step (pairs such as `aux1×sperson`, `rform×you`): enumerate slot=value pairs and triples over the silver, keep those whose association with a label passes a G² test and adds over their parts, and add only these as conjunction features. Measure act and indirect macro-F1 in `prag eval` with the shuffled-label control; the mined list itself is an explanation of what the model may use.
- I2. For coreference, mine discriminative patterns over mention-pair features (mention type, agreement, Hobbs rank, sieve that fired, distance bucket) to find conjunctions that predict wrong links of the current sieves, e.g. «`it` + candidate is a time/number + distance > 1 sentence». Mining must run on licence-clean data or the teacher's silver; on GUM it may only diagnose, not supply rules (GUM is NC and dev-only). Turn each pattern that is linguistically sensible into a hand-written blocker in `coref/src/sieve.rs` and measure its precision on GUM dev.
- I3. Prefer the paper's four carefully built pairwise features (agreement, modifier compatibility, salience-weighted distance) over raw lexical ones in any future ranker; they carry across domains.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §8–9; `31-speech-acts.md` §9 (act features). Code: `prag/src/feats.rs` and `prag/src/model.rs` (mined conjunctions), `coref/src/sieve.rs` (blockers). Measures: `prag eval` macro-F1; sieve precision and CoNLL F1 on GUM dev.

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/d18-1018](https://doi.org/10.18653/v1/d18-1018) · [arXiv 1708.00160](https://arxiv.org/abs/1708.00160)

```
@inproceedings{moosavi2018using,
  title = {{Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers}},
  author = {Moosavi, Nafise Sadat and Strube, Michael},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1018},
  eprint = {1708.00160},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d18-1018}
}
```

- [← previous Lexical Features in Coreference Resolution: To be Used With Caution](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2017-lexical-features-coreference-resolution-be/)
- [next → Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2019-automatically-extracted-minimum-spans-disentangle/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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