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# Idiomify -- Building a Collocation-supplemented Reverse Dictionary of English Idioms with Word2Vec for non-native learners

**Authors:** Eu-Bin Kim · **Year:** 2022 · **Venue:** arXiv:2204.05634 (undergraduate report, University of Manchester, May 2021) **Link:** [arxiv.org/abs/2204.05634](https://arxiv.org/abs/2204.05634) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (arXiv)

[**0** citations](https://openalex.org/works?filter=cites:W4223964181) openalex · 2026-10-18[arXiv:2204.05634](https://openalex.org/S4306400194)[licence](http://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://arxiv.org/abs/2204.05634)
- [PDF](https://arxiv.org/pdf/2204.05634)
- [arXiv 2204.05634](https://arxiv.org/abs/2204.05634)
- [OpenAlex](https://openalex.org/W4223964181)

**Authors (1)**

1. [Eu-Bin Kim](https://openalex.org/A5030973067) h 0 · 0 citations · 1 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 project builds a reverse dictionary for idioms, so that a learner can find an idiom from a phrase or a definition, and adds collocates of each idiom so the learner can use it precisely. Idioms are taken from the SLIDE list of frequent idioms, and 2,746 are kept after dropping short ones. Idiom matching rules are generated from base forms and cover three of the six variation types: optional hyphen, inflection and alternative wording. Modification, passivisation and open slot fail, although the author shows how slop, wildcard and reordering could cover them. Idioms are found in over 3 million utterances of spoken corpora. Collocates are ranked by term frequency, PMI and TF-IDF; on five idioms with dictionary example sentences as the reference, PMI and TF-IDF rank the collocates better than frequency, with no clear winner between them. The reverse search averages word2vec vectors of the phrase and returns the nearest idioms; its median rank of 128 on a 100-idiom test set is not comparable with the Hill et al. baselines, as the author notes. Removing stopwords makes the median rank worse (128 against 156).

## Ideas

- I1. `en/src/morph.rs`: the matching rules as explicit lemma patterns with alternatives and an optional hyphen, written as data, with one test per variation type. Add slop (a word gap limit) for modification, and a reordered pattern for passives, with a negative control that must not fire on unrelated sentences.
- I2. [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/): keep stopwords in the idiom-to-definition index. The ablation in the paper (128 against 156 median rank) is the first measure to repeat with Mova's own idioms.
- I3. Count-based collocates: compute PMI and frequency from counts in a BTreeMap, not a library model, and check the ranking against dictionary example sentences, with a shuffled-count control.
- I4. A hybrid search: an inverted index of definitions first, then a ranking by the phrase vector only when the index is empty. The author proposes this as future work; it is also the explainable order.

## For Mova

The chapters are [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) (idioms and collocations) and [34. Irony and figurative language](https://md.mova.elamur.ai/free/en/docs/languages/en/34-irony-and-figurative-language/) (figurative use). The code points are `en/src/morph.rs` for the matching rules, `world/src/dictionary.rs` for definitions, and the pupil dictionary in `en/src/dict.rs`. The measure: the match rate per variation type on a held-out idiom list, and the median rank of the definition search with and without stopwords, run twice for determinism.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2204.05634](https://arxiv.org/abs/2204.05634)

```
@misc{kim2022idiomify,
  title = {{Idiomify -- Building a Collocation-supplemented Reverse Dictionary of English Idioms with Word2Vec for non-native learners}},
  author = {Kim, Eu-Bin},
  year = {2022},
  eprint = {2204.05634},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2204.05634}
}
```

- [← previous Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://md.mova.elamur.ai/free/en/docs/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
- [next → "Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures](https://md.mova.elamur.ai/free/en/docs/papers/kim-2023-pope-catholic-applying-chain-thought/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

16 references, 1 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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8. Hughes, C. Filimonov, Maxim. Wray, Alison. Spasi´c, Irena. (2021). Leaving No Stone Unturned: Flexible Retrieval of Idiomatic Expressions from a Large Text Corpus. [find](https://scholar.google.com/scholar?q=Hughes%2C+C%2E+Filimonov%2C+Maxim%2E+Wray%2C+Alison%2E+Spasi%C2%B4c%2C+Irena%2E+%282021%29%2E+Leaving+No+Stone+Unturned%3A+Flexible+Retrieval+of+Idiomatic+Expressions+from+a+Large+Text+Corpus%2E)
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11. Manning, D., et al. (2008). Tf-idf weighting. Viewed on 23th of February 2021. Available at: [https://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html](https://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html) Miller, G. (1995). WordNet: A Lexical Database for English. Communications of the ACM Vol. 38, No. 11: 39-41. [↗](https://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lex-1994-miller-wordnet/)
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