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# Classification of Verb-Particle Constructions with the Google Web1T Corpus

**Authors:** [Jonathan K. Kummerfeld](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonathan-k-kummerfeld/), [James R. Curran](https://md.mova.elamur.ai/free/en/docs/papers/authors/james-r-curran/) · **Year:** 2008 · **Venue:** Proceedings of the Australasian Language Technology Association Workshop 2008 **Link:** [aclanthology.org/U08-1008](https://aclanthology.org/U08-1008/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**4** citations](https://openalex.org/works?filter=cites:W2252242540) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2008-kummerfeld-classification-verb-particle-constructions-google/) of the 140673 papers whose bibliographies we read[Proceedings of the Australasian Language Technology Association Workshop 2008](https://aclanthology.org/volumes/U08-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/U08-1008/)
- [PDF](https://aclanthology.org/U08-1008.pdf)
- [OpenAlex](https://openalex.org/W2252242540)

**Authors (2)**

1. [Jonathan K. Kummerfeld](https://openalex.org/A5057970781) [ORCID](https://orcid.org/0000-0001-5030-3016) · h 22 · 2132 citations · 107 works
2. James R. Curran [ORCID](https://orcid.org/0000-0001-9637-0484)

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 builds a type-level classifier for verb-particle constructions (VPCs), with no parser and no sentence context. For each verb-particle pair it runs the fronting test: it compares the Web1T n-gram counts of the two orders, "verb particle NP" and "verb NP particle", and decides by a learned rule. The evaluation uses 2,000 true VPCs and 2,000 non-VPC pairs. The classifier reaches an F-score of 75.7%, with accuracy 74.7%, recall 74.7% and precision 76.8%, and it handles thousands of queries a second. The authors say that a parser-based method reaches 97.4% F, but only on frequent VPCs in context, so the lightweight method trades accuracy for coverage.

## Ideas

- I1. Fronting counts for `en`: for each verb-particle candidate from the Mova raw collection, count the two orders in a BTreeMap table and classify with the count ratio. Measure: F on a hand-built set of 2,000 EWT verb-particle pairs and 2,000 non-pairs, with two identical runs.
- I2. Use the count ratio as a feature for `en/src/parse.rs` at the `compound:prt` decision. Measure: LAS on EWT test sentences with a particle, with and without it.
- I3. Check the coverage problem first: the share of candidate pairs with zero counts in the Mova collection. The paper's method needs counts, and a zero-count pair cannot be decided. Measure before any classifier is built.

## For Mova

The chapter is [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/), with [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) for the particle verbs. The code is `en/src/lexgen.rs` (the pair list) and `en/src/parse.rs` (the feature). Web1T is not in Mova; build the counts from the Mova collection. The 2,000-pair sets come from NC resources, so they are for tests only.

## Cite

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

```
@inproceedings{kummerfeld2008classification,
  title = {{Classification of Verb-Particle Constructions with the Google Web1T Corpus}},
  author = {Kummerfeld, Jonathan K. and Curran, James R.},
  booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2008},
  year = {2008},
  url = {https://aclanthology.org/U08-1008/}
}
```

- [← previous Adapting a WSJ-Trained Parser to Grammatically Noisy Text](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2008-foster-adapting-wsj-trained-parser-grammatically/)
- [next → Task-oriented Evaluation of Syntactic Parsers and Their Representations](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2008-miyao-task-oriented-evaluation-syntactic-parsers/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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