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# Meaning Representation of English Prepositional Phrase Roles: SNACS Supersenses vs. Tectogrammatical Functors

**Authors:** [Wesley Scivetti](https://md.mova.elamur.ai/free/en/docs/papers/authors/wesley-scivetti/), [Nathan Schneider](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathan-schneider/) · **Year:** 2023 · **Venue:** DMR 2023 (Fourth International Workshop on Designing Meaning Representations) **Link:** [aclanthology.org/2023.dmr-1.7](https://aclanthology.org/2023.dmr-1.7/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); STREUSLE (SNACS-annotated EWT reviews) is CC BY-SA — tests only

[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/scivetti-2023-meaning-representation-english-prepositional-phrase/) of the 140673 papers whose bibliographies we read[Proceedings of the Fourth International Workshop on Designing Meaning Representations](https://aclanthology.org/volumes/2023.dmr-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2023.dmr-1.7/)
- [PDF](https://aclanthology.org/2023.dmr-1.7.pdf)

**Authors (2)**

1. Wesley Scivetti
2. Nathan Schneider

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

## What it is about

The paper compares two inventories for the meaning of prepositional phrases: SNACS supersenses (52 labels in three branches — circumstance: Locus, Time, Duration, Source, Goal, Direction, Manner, Purpose…; participant: Agent, Theme, Recipient, Beneficiary, Instrument, Ancillary, Cost…; configuration: relations between nominals like QuantityItem, Possessor), each preposition carrying a scene role and a function role, and the functors of the Prague English Dependency Treebank (LOC, TWHEN, THL, DIR1/DIR3, MEANS, ACMP, BEN…). From the guidelines they write a heuristic functor→supersense mapping (DIR1 → Source, DIR3 → Goal, THL → Duration), then run a SNACS classifier on 1,837 PPs in 838 WSJ sentences of PEDT. Agreement with the mapping is about 50% overall, best for temporal (66.5%) and agent/patient-like ones, worst for configurations (36%). Mismatches come partly from classifier errors (money read as Cost, a month as Locus) and partly from real differences: fictive motion («60 miles from Seoul» is a Locus scene with a Source function), *with* + partner as Agent scene with Ancillary function.

## Ideas

- I1. Give Mova's prepositional obliques a small set of supersenses chosen by deterministic rules instead of the bare preposition (`obls` holds `with=queen`): Source (*from*, *out of*), Goal (*to/into/onto* after a motion verb), Locus (*in/on/at/under* + place), Time and Duration (*for* + time noun), Instrument (*with* + inanimate), Ancillary/companion (*with* + animate), Beneficiary (*for* + animate), Topic (*about*), Purpose (*for* + event noun), Cost (*for* + money). The rule and the head's animacy go into `How`. Then «with whom», «what with», «for whom», «from where» questions map to one slot. Measure frame bench obliques/place F1 and the to-whom/with-whom course questions (`world knowledge diagnose`).
- I2. Keep scene and function apart where they diverge, as SNACS does: «a cave a mile from the village» is where the cave is (Locus), not where something came from; *with* + animate under a verb of joint action (*fight with*, *play with*) is a co-agent, not an instrument. Write these as reader tests, each with the wrong reading as the negative control.
- I3. Measure the rule set of I1 against STREUSLE's gold supersenses (tests only, outside the repo, like PropBank), on scene roles, per preposition. It shows which prepositions need more than one rule.

## For Mova

[22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) (prepositional phrases), `23-complementation.md`, `30-pragmatics-overview.md` §5.3. Code: `prag/src/frames.rs` (obliques), `prag/src/vn.rs` (VerbNet PP roles: Destination, Source, Instrument already used for place/not-place), `world/src/knowledge.rs`. Measures: frame bench place/oblique F1, course questions with prepositional wh-phrases, per-preposition accuracy on STREUSLE.

## Cite

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

```
@inproceedings{scivetti2023meaning,
  title = {{Meaning Representation of English Prepositional Phrase Roles: SNACS Supersenses vs. Tectogrammatical Functors}},
  author = {Scivetti, Wesley and Schneider, Nathan},
  booktitle = {Proceedings of the Fourth International Workshop on Designing Meaning Representations},
  year = {2023},
  url = {https://aclanthology.org/2023.dmr-1.7/}
}
```

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