---
title: "Glen, Glenda or Glendale — paper by Bergsma et al. (2009)"
description: "English third-person pronouns signal gender and number, so a pronoun resolver can… 95.5% accuracy. 30 references, cited by 6 papers here."
authors: ["Shane Bergsma", "Dekang Lin", "Randy Goebel"]
year: 2009
venue: "Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)"
source: "https://aclanthology.org/W09-1116/"
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/en/papers/bergsma-2009-glen-glenda-or-glendale-unsupervised/"
---
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# Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender

**Authors:** [Shane Bergsma](https://md.research.elamur.ai/free/en/authors/shane-bergsma/), [Dekang Lin](https://md.research.elamur.ai/free/en/authors/dekang-lin/) and Randy Goebel · **Year:** 2009 · **Venue:** [Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)](https://md.research.elamur.ai/free/en/venues/conll/) ([CoNLL 2009](https://md.research.elamur.ai/free/en/venues/conll/2009/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/W09-1116/) · [PDF](https://aclanthology.org/W09-1116.pdf) · [OpenAlex](https://openalex.org/W1966875675)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/bergsma-2009-glen-glenda-or-glendale-unsupervised/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
English third-person pronouns signal gender and number, so a pronoun resolver can discard candidate nouns that do not match. Earlier gender models counted how often each noun type co-occurred with pronouns of each class. Rare and unseen nouns therefore got no reliable estimate. Ambiguous types such as *lawyer* or *Ford* were also given a single label regardless of context.

The authors do not use these counts as the final answer. Instead they filter the Bergsma and Lin (2006) gender database down to a set of very reliable "pseudo-seed" nouns. They find occurrences of these nouns in about a million documents and use the seed genders to auto-label training data for a 4-way discriminative classifier (masculine, feminine, neutral, plural). All mentions of a noun within one document are classified together, using context features and type-level features (morphological and categorical cues).

A semi-supervised variant combines the classifier's output with the original counts, tuned on a small labeled set. It reaches 95.5% accuracy, roughly halving the error of the prior state of the art. The method needs no manual labels to train the base model and can handle unseen words.

## Key points
- **Base resource:** the path-based gender database of Bergsma and Lin (2006). It lists masculine, feminine, neutral and plural pronoun counts for 3.1 million lower-cased nouns. A log-count classifier over this database serves as the baseline, PATHGENDER.
- **Pseudo-seed filtering:** nouns are kept only if they have at least 50 counts, their most-frequent pronoun gender (MFPG) covers at least 85% of those counts, and their database-to-web count ratio clears a threshold. About 45 thousand seed nouns remain.
- **Training examples:**
  - Noun mentions are grouped by lower-case string within each document, with ±5 tokens of context per mention.
  - At most 200 groups are kept per noun string, so frequent nouns do not dominate.
  - Only features seen more than 5 times are retained.
- **Main result:** FULL-SEMI achieves 95.5% accuracy on 2596 test nouns, a 50% error reduction over prior work.
- **Post-processing:** a surname-matching step raises FULL-SEMI to 96.7%, PATHGENDER to 93.3% and PATHGENDER+ to 94.3%. Remaining errors include collective nouns such as *United States*, *group* and *public*, which were labeled plural but predicted neutral.

## Interesting ideas
- An imperfect, sparse statistical resource is used as a labeler rather than as the final model: only its most confident entries become training labels for a richer classifier that generalizes to rare and unseen items.
- Seed reliability is checked by comparing how often a word appears in the coreference database with how often it appears on the web. Words that are frequent overall but rarely act as antecedents (like *importance*) get flagged as untrustworthy, which hints at how "referential" a noun is.
- All mentions of the same noun string in a document are pooled into one composite vector, relying on the one-sense-per-discourse tendency. This yields one consistent decision per entity and more context evidence.
- Gender is treated as a property of the referent in context, not of the word type. The same noun (*lawyer*, *Ford*) can therefore get different classes in different documents.
- A cheap name-matching post-processor lets a later short mention (a surname) inherit the gender of an earlier full name. It helped every system tested.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Mova's silver-data pipeline could keep only entries where witness parsers or count statistics agree strongly, then train a feature-rich model that also covers words the statistics never saw.
- **I2** (frames): the coreference component could use a similar ratio as a prior for whether a noun is a plausible pronoun antecedent at all, pruning candidates before agreement checks.
- **I3** (world): when building the entity list of a text, gender, number and type evidence from every mention could be pooled into one entity record, so a single clear cue (e.g. *he says*) settles the entity throughout the story.
- **I4** (world): entity attributes like gender or animacy should live on the text's entities with their evidence chain, with lexicon defaults used only as overridable priors, which keeps decisions explainable.
- **I5** (frames): coreference could add an explicit rule linking surname mentions to earlier full names and copying agreement features, a transparent step that is easy to explain in the answer trace.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 492 works · 440,463 citations
- [University of Alberta](https://md.research.elamur.ai/free/en/universities/university-of-alberta/) — University, Edmonton, Canada · 51 works · 5,017 citations

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## Cite

Cite as: Shane Bergsma, Dekang Lin and Randy Goebel. 2009. Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender. Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009).

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

```bibtex
@inproceedings{bergsma2009glen,
  title = {{Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender}},
  author = {Shane Bergsma, Dekang Lin and Randy Goebel},
  booktitle = {Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)},
  year = {2009},
  doi = {10.3115/1596374.1596396},
  url = {https://doi.org/10.3115/1596374.1596396}
}
```

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## References

30 references, 11 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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*Updated 1 November 2026*