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# Simple Models for Word Formation in English Slang

**Authors:** Vivek Kulkarni, William Yang Wang · **Year:** 2018 · **Venue:** arXiv:1804.02596 **Link:** [arxiv.org/abs/1804.02596](https://arxiv.org/abs/1804.02596) · **Licence:** arXiv — non-exclusive licence to distribute — summary only

[**5** citations](https://openalex.org/works?filter=cites:W2796299569) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kulkarni-2018-simple-models-word-formation-english/) of the 140673 papers whose bibliographies we read[arXiv:1804.02596](https://openalex.org/S4306400194)[licence](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

- [Publisher's page](https://arxiv.org/abs/1804.02596)
- [PDF](https://arxiv.org/pdf/1804.02596)
- [arXiv 1804.02596](https://arxiv.org/abs/1804.02596)
- [OpenAlex](https://openalex.org/W2796299569)

**Authors (2)**

1. [Vivek Kulkarni](https://openalex.org/A5013097792) [ORCID](https://orcid.org/0000-0003-2926-3747) · h 18 · 2852 citations · 71 works
2. [William Yang Wang](https://openalex.org/A5100702485) [ORCID](https://orcid.org/0000-0001-6153-8240) · h 65 · 19499 citations · 668 works

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

## What it is about

The paper models three kinds of irregular word formation in English slang: blends (such as "fantabulous" from "fantastic" and "fabulous"), clippings (such as "prof" from "professor"), and reduplicatives (such as "boo-boo"). For blends it uses a character-level sequence model (CopyCat-style, with a language-model term and a length term) to choose which parts of the two source words to keep, and it reports edit distance on two datasets. On the D_knight blend data the single model scores 1.98, its ensemble 1.91, and the best prior model 1.76. For clippings it uses a phoneme-aware and a grapheme-based probabilistic model on 173 clippings, with mean edit distance 3.39 and 2.65 against a naive baseline of 4.6. For reduplicatives it uses a generative model with phonetic constraints and reports MIR on 50 held-out items; it beats the baselines by at least 8 percentage points. Most blend outputs (99.0% on D_knight) use only characters that appear in the two source words.

## Ideas

- I1. Blend check in `en/src/morph.rs`: a candidate blend is valid only if all its characters come from the two source words, in order, and its length is at most the longer source. The paper finds 99% of blends obey this, so use the check as a rule and count the exceptions.
- I2. Reduplicative rule table: encode the patterns the paper names (duplication, and the exchange of the initial vowel or consonant) as a closed set of rules in `en/src/morph.rs`, each with a count of matches in the open text, so every reduplicative can say which pattern fired.
- I3. Clipping as a deterministic choice of cut point: among the cut points that keep a stressed syllable, choose the one with the highest count in Mova's open text, and report the edit distance against a fixed clipping list.
- I4. Negative control: give the blend rule two unrelated words; the rule must find no valid blend, or the character check is not doing anything.

## For Mova

The chapters are [18. Compounding, conversion and the minor processes](https://md.mova.elamur.ai/free/en/docs/languages/en/18-compounding-conversion-minor/) (blends, clippings, reduplicatives as minor word formation). The code is `en/src/morph.rs`. The measure: the rate of valid blends under the character rule, and mean edit distance on a held-out list, run twice. The paper's data and text are licensed for reading only: use the summary and the design, and build the rule tables from Mova's own open sources.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 1804.02596](https://arxiv.org/abs/1804.02596)

```
@misc{kulkarni2018simple,
  title = {{Simple Models for Word Formation in English Slang}},
  author = {Kulkarni, Vivek and Wang, William Yang},
  year = {2018},
  eprint = {1804.02596},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1804.02596}
}
```

- [← previous Parsing Early Modern English for Linguistic Search](https://md.mova.elamur.ai/free/en/docs/papers/kulick-2022-parsing-early-modern-english-linguistic/)
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