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# Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns

**Authors:** Kellie Webster, [Marta Recasens](https://md.mova.elamur.ai/free/en/docs/papers/authors/marta-recasens/), Vera Axelrod, [Jason Baldridge](https://md.mova.elamur.ai/free/en/docs/papers/authors/jason-baldridge/) · **Year:** 2018 · **Venue:** TACL 6 **Link:** [aclanthology.org/Q18-1042](https://aclanthology.org/Q18-1042/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the GAP data's licence is stated in its own release — to be checked before use

[**216** citations](https://openalex.org/works?filter=cites:W2920114910) openalex-snapshot · 2026-10-14[**142** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/webster-2018-mind-gap-balanced-corpus-gendered/) of the 140673 papers whose bibliographies we read[Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/Q18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/Q18-1042/)
- [PDF](https://aclanthology.org/Q18-1042.pdf)
- [DOI 10.1162/tacl_a_00240](https://doi.org/10.1162/tacl_a_00240)
- [OpenAlex](https://openalex.org/W2920114910)

**Authors (4)**

1. [Kellie Webster](https://openalex.org/A5088842522) h 13 · 1418 citations · 31 works
2. [Marta Recasens](https://openalex.org/A5103978745) h 19 · 2086 citations · 72 works
3. [Vera Axelrod](https://openalex.org/A5090245882) h 8 · 668 citations · 11 works
4. [Jason Baldridge](https://openalex.org/A5048931310) [ORCID](https://orcid.org/0000-0002-4712-1841) · h 45 · 7433 citations · 135 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

GAP is 8,908 pronoun–name pairs (4,454 contexts) from Wikipedia, each with a gendered pronoun and two names of the same gender, either of which (or neither) could be the referent, with no other compatible mention in between. Contexts are extracted by three patterns (both names before the pronoun in one sentence; pronoun in an initial subordinate clause or prepositional possessive; all in one sentence) and balanced for gender (1:1, though Wikipedia gives 9:1 masculine), for which name is right, and for whether the page's own entity is involved. Three raters per example agree well (kappa 0.74). Off-the-shelf resolvers trained on OntoNotes (rule-based Stanford, three neural ones) do poorly and are biased: feminine pronouns are resolved worse. Simple cue baselines help separate the factors: token distance and «topical entity» are weak, syntactic distance and especially parallelism (same grammatical role as the pronoun) are strong; a Transformer-attention baseline combined with parallelism is best, at 66.9 F1 overall in the snippet setting, more with the page title.

## Ideas

- I1. Use GAP (if its licence permits evaluation) as a targeted test of the `pronoun` sieve where gender cannot decide, which is exactly where Hobbs order alone must work: report F1 overall and masculine vs feminine, and compare with the paper's parallelism and syntactic-distance baselines. Mova does not guess gender from names, so a gender gap in its scores would point to the noun lists or to animacy evidence.
- I2. Add a parallelism preference to the `pronoun` sieve and to the frame reader's salience: a subject pronoun prefers a subject antecedent, an object pronoun an object one (the +35 parallelism weight of Lappin & Leass, already noted in [coref — SLM coreference: a sieve of rules with a reason for every link](https://md.mova.elamur.ai/free/en/docs/coref/) «What next»). Measure per-pronoun precision for *he/she/him/her* on GUM dev and on GAP.
- I3. The page-entity effect has a tale analogue: the title character of a tale is the default referent of an otherwise ambiguous *he/she* at paragraph start. Give the title character a standing salience bonus in `prag/src/frames.rs` `Ctx` and measure frame bench coref on tale lines.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §2 (salience), §8 (algorithms), §9. Code: `coref/src/sieve.rs` (`pronoun` sieve, parallelism), `prag/src/frames.rs` (salience). Measures: GAP F1 by gender; per-pronoun precision on GUM dev; frame bench coref F1.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00240](https://doi.org/10.1162/tacl_a_00240)

```
@article{webster2018mind,
  title = {{Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns}},
  author = {Webster, Kellie and Recasens, Marta and Axelrod, Vera and Baldridge, Jason},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2018},
  doi = {10.1162/tacl\_a\_00240},
  url = {https://doi.org/10.1162/tacl_a_00240}
}
```

- [← previous Anaphora and Discourse Structure](https://md.mova.elamur.ai/free/en/docs/papers/webber-2003-anaphora-discourse-structure/)
- [next → Committed Belief Tagging on the Factbank and LU Corpora: A Comparative Study](https://md.mova.elamur.ai/free/en/docs/papers/werner-2015-committed-belief-tagging-factbank-lu/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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