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# Control vs. Raising in English. A Dependency Grammar Account

**Authors:** [Timothy Osborne](https://md.mova.elamur.ai/free/en/docs/papers/authors/timothy-osborne/) and Matthew Reeve · **Year:** 2017 · **Venue:** Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017) **Link:** [aclanthology.org/W17-6521](https://aclanthology.org/W17-6521/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)](https://aclanthology.org/volumes/W17-65/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W17-6521/)
- [PDF](https://aclanthology.org/W17-6521.pdf)

**Authors (2)**

1. Timothy Osborne
2. Matthew Reeve

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper argues that a dependency grammar cannot tell control predicates (*prefer*, *ask*, *pretty*) from raising predicates (*seem*, *expect*, *tough*) by tree shape alone, because both take a to-infinitive and get the same flat structure. The difference is placed in the valency frames of the predicate lexicon. Each valent is marked as an argument or not, as semantically selected or not, and as raised or controlled. Diagnostics support this: passivization, *there*-insertion, topicalization, clefting and reflexivization all treat "Jim to stay" as a non-constituent for raising and control verbs alike, but differ in what the matrix predicate selects. The paper sorts English control and raising into eight types (subject-to-subject, subject-to-object, object-to-subject, object-to-object, and their raising counterparts) and gives valency frames for each. It contains no corpus counts and no experiment. The worked examples are the material.

## Ideas

- I1. A control/raising flag in the English lexicon: `en/src/dict.rs` gets one enum per predicate (the eight types, not strings) with a flag for whether its valent is semantically selected. The enum is built into the dictionary at build time, so it is deterministic.
- I2. Golden diagnostic tests: take the paper's minimal sets (passive, *there*-insertion, topicalization, clefting, reflexive) and turn each into a unit test. The parser and frames must accept the grammatical forms and reject the starred ones; a shuffled-verb control must fail.
- I3. Who-did-what inference in `prag`: for a control verb, the matrix subject is the understood subject of the embedded predicate ("Sam wanted to stop" gives Sam as the stopper), while for a raising verb it is not. Implement this in `prag/src/roles.rs` and check it on school stories with who-question items.
- I4. Tough-movement and object-control rules: for "This couch is tough to move", the object of *move* is *couch*. Record these as rules in `prag/src/frames.rs` and count how many school sentences they fire on.
- I5. Keep the paper's refusal of null elements: do not add PRO or traces to the parser; the controller is a lexicon flag resolved in the frame, which keeps the output explainable.

## For Mova

Main chapters: [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/) (control and raising), [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) (passive diagnostic), [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/) (topicalization, clefting), [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) (to-infinitive phrases). Code: `en/src/dict.rs` (predicate flags), `prag/src/roles.rs` and `prag/src/frames.rs` (inference of the understood subject and object). Measure: the number of diagnostic tests passing, and the accuracy of who-question items with control and raising verbs, run twice to confirm determinism.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/)

## Cite

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

```
@inproceedings{osborne2017control,
  title = {{Control vs. Raising in English. A Dependency Grammar Account}},
  author = {Osborne, Timothy and Reeve, Matthew},
  booktitle = {Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)},
  year = {2017},
  url = {https://aclanthology.org/W17-6521/}
}
```

- [← previous Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities](https://md.mova.elamur.ai/free/en/docs/papers/orlando-2023-exploring-non-verbal-predicates-semantic/)
- [next → Predicate Catenae: A Dependency Grammar Analysis of It-Clefts](https://md.mova.elamur.ai/free/en/docs/papers/osborne-2019-predicate-catenae-dependency-grammar-analysis/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

11 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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