---
title: "A Key Word Analysis of English Intensifying Adverbs in Male and"
description: "The authors take the spoken, gender-annotated portion of ICE-GB and use ICECUP 3.1 to build a… 17 references, cited by 2 papers here."
authors: ["Jieun Jeon", "Jae-Woong Choe"]
year: 2009
venue: "Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1"
source: "https://aclanthology.org/Y09-1023/"
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/jeon-2009-key-word-analysis-english-intensifying/"
---
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# A Key Word Analysis of English Intensifying Adverbs in Male and Female Speech in ICE-GB

**Authors:** Jieun Jeon and [Jae-Woong Choe](https://md.research.elamur.ai/free/en/authors/jae-woong-choe/) · **Year:** 2009 · **Venue:** [Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1](https://md.research.elamur.ai/free/en/venues/paclic/) ([PACLIC 2009](https://md.research.elamur.ai/free/en/venues/paclic/2009/)) · **Publisher:** [City University of Hong Kong](https://md.research.elamur.ai/free/en/publishers/city-university-of-hong-kong/) · **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/Y09-1023/) · [PDF](https://aclanthology.org/Y09-1023.pdf) · [OpenAlex](https://openalex.org/W2250237081)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tests Lakoff's (1975) claim that women use more hedges and intensifiers. Instead of relying on introspection, it measures the question in a corpus. The authors take the spoken, gender-annotated portion of ICE-GB and use ICECUP 3.1 to build a female and a male subcorpus. Every word that is not an intensifying adverb is replaced by its category tag. The two subcorpora are then compared with the KeyWords facility of WordSmith, using chi-square at p=0.01. Raw frequency lists turn out to be almost the same for both genders, but keyness analysis separates them. Female key items such as *really*, *sort of*, *so* and *a bit* tend to be hedge-like or emotional. Male key items such as *entirely*, *around* and *over* point to scale extremes or appear next to numbers. The second contribution is methodological. The authors vary the size and makeup of the reference corpus and check dispersion. They show that the choice of reference corpus strongly changes which key words come out, so this choice should be treated as a deliberate experimental decision.

## Key points
- Data: ICE-GB (about 1M words, 200 written and 300 spoken texts). The female subcorpus has 532 speakers and 287,434 words extracted. The male subcorpus has 1,116 speakers and 584,497 words.
- Intensifying adverbs: the female subcorpus has 180 types and 4,614 tokens; the male subcorpus has 251 types and 8,226 tokens. The two share 140 types. *very* is the most frequent in both, and the top-10 lists differ by only one item each.
- Final key words, listed alphabetically: the female subcorpus yields 13 (*a bit, all, at all, completely, much, quite, really, slightly, so, sort of, that, too, very*), while the male subcorpus yields 7 (*around, entirely, over, relatively, right, some, well*).
- Reference composition matters. With size ratios kept near 1, excluding the target from the reference gave 13 female and 7 male key words. Including it (whole-corpus sample) gave only 7 and 2. Smaller references performed worse, and a previously recommended 1:5 ratio did not work as expected.
- Log-likelihood produced a few extra key words, but these were poorly dispersed, so chi-square was preferred.
- Dispersion of female key words was above 0.6, well over the 0.4 cutoff from Oakes and Farrow. Some male key words were unstable across subsamples; for example, the dispersion of *well* fell from 0.825 to 0.426.

## Interesting ideas
- Merging multiword adverbs such as *sort of* and *a bit* into one unit before counting keeps their meaning from being split into misleading counts of separate words.
- Reducing every non-target word to its category tag keeps the corpus structure while focusing the statistics on the lexical class of interest.
- A keyness or contrast score should be checked against a reference that excludes the target and is matched in size, and the result should be rechecked on random subsamples.
- Frequency alone is not enough: an item should only count as characteristic if it is spread evenly across many texts rather than concentrated in a single document.
- Degree adverbs can be placed on a single scale of speaker commitment, with amplifiers (*very, so*) at the high end and downtoners or hedges (*a bit, sort of*) at the low end.

## For Mova

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

- **I1** (tokens): the tokenizer/lemmatizer could recognise degree-modifier MWEs as single units, so the tagger and later stages see one adverb rather than a noun plus a preposition.
- **I2** (learn): when comparing silver-data slices, masking everything except the phenomenon under study could make diagnostic statistics cleaner and easier to explain.
- **I3** (learn): evaluation with negative controls could compare a parser or rule's behaviour on matched, disjoint subsets, so that apparent gains are not artifacts of how the subsets overlap or of their size.
- **I4** (learn): self-induced repair rules could be accepted only when their triggering cases are dispersed across documents, which filters out rules that fit one odd text.
- **I5** (frames): prag could attach a commitment-strength value to assertions based on these modifiers, which feeds hedging, implicature and irony detection.

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

## Institutions

- [Korea University](https://md.research.elamur.ai/free/en/universities/korea-university/) — University, Seoul, South Korea · 26 works · 9,490 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works

## Cite

Cite as: Jieun Jeon and Jae-Woong Choe. 2009. A Key Word Analysis of English Intensifying Adverbs in Male and Female Speech in ICE-GB. Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1.

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

```bibtex
@inproceedings{jeon2009key,
  title = {{A Key Word Analysis of English Intensifying Adverbs in Male and Female Speech in ICE-GB}},
  author = {Jieun Jeon and Jae-Woong Choe},
  booktitle = {Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1},
  year = {2009},
  url = {https://aclanthology.org/Y09-1023/}
}
```

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## References

17 references, 0 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*