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# Differentiating Types of Verb Particle Constructions

**Authors:** Jon Patrick, Jeremy Fletcher · **Year:** 2004 · **Venue:** Proceedings of the Australasian Language Technology Workshop 2004 **Link:** [aclanthology.org/U04-1022](https://aclanthology.org/U04-1022/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**3** citations](https://openalex.org/works?filter=cites:W2252251330) openalex · 2026-10-18[Proceedings of the Australasian Language Technology Workshop 2004](https://aclanthology.org/volumes/U04-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/U04-1022/)
- [PDF](https://aclanthology.org/U04-1022.pdf)
- [OpenAlex](https://openalex.org/W2252251330)

**Authors (2)**

1. [Jon Patrick](https://openalex.org/A5052573759) [ORCID](https://orcid.org/0000-0001-6234-9740) · h 19 · 1694 citations · 121 works
2. [Jeremy Fletcher](https://openalex.org/A5025325300) h 2 · 16 citations · 3 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 takes a verb-particle construction (VPC) scheme from linguistic sources and asks whether it separates the meanings of phrasal verbs in context. The scheme has three classes: decomposable (about 44% of the data), non-decomposable (about 36%) and independent (about 19%). The data is a British National Corpus subset of about 6,000 sentences from roughly 70 verb-particle pairs, plus 400 sentences that a first classifier got wrong and that the authors tagged by hand. Features come from the sentence: the particle, the verb-particle distance, the tags of the surrounding noun phrases, and the verb's syllable count. The majority-class baseline is F 0.613 on the combined set, and the feature experiments are compared against it step by step. The paper also reports particle-tag F scores of 0.969, 0.990 and 0.833 for the three CLAWS adverb and preposition tags.

## Ideas

- I1. Particle sense as a rule in `en/src/expert.rs`: separate adverbial from prepositional particles by the distance to the object and by the noun-phrase head tags. Measure: label accuracy on EWT test verb-particle cases, against the current `en/src/ptag.rs` tags, with two identical runs.
- I2. Use verb-particle distance as a feature in `en/src/parse.rs` when it decides between `compound:prt` and `case` for a particle. Measure: LAS on EWT test sentences with a particle, with and without the feature.
- I3. Test the syllable and Germanic-origin idea only as a probe: count how often a monosyllabic verb predicts the non-decomposable class in the BNC subset. Measure before anything enters the grammar. The data are NC, so the probe is test only.

## For Mova

The chapter is [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) only for particle objects; the main home is [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/) and [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/). The code is `en/src/expert.rs` and `en/src/parse.rs`. The BNC subset is NC, so use it for tests only; numbers must come from EWT test.

## Cite

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

```
@inproceedings{patrick2004differentiating,
  title = {{Differentiating Types of Verb Particle Constructions}},
  author = {Patrick, Jon and Fletcher, Jeremy},
  booktitle = {Proceedings of the Australasian Language Technology Workshop 2004},
  year = {2004},
  url = {https://aclanthology.org/U04-1022/}
}
```

- [← previous Deterministic Dependency Parsing of English Text](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2004-nivre-deterministic-dependency-parsing-english-text/)
- [next → Supersense Tagging of Unknown Nouns using Semantic Similarity](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-curran-supersense-tagging-unknown-nouns-semantic/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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28 references, 2 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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