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# Prepositional Phrase Attachment without Oracles

**Authors:** Michaela Atterer and [Hinrich Schütze](https://md.mova.elamur.ai/free/en/docs/papers/authors/hinrich-schutze/) · **Year:** 2007 · **Venue:** Computational Linguistics, 33(4), squib **Link:** [aclanthology.org/J07-4002](https://aclanthology.org/J07-4002/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**28** citations](https://openalex.org/works?filter=cites:W2075790020) openalex · 2026-10-19[Computational Linguistics](https://aclanthology.org/volumes/J07-4/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J07-4002/)
- [PDF](https://aclanthology.org/J07-4002.pdf)
- [DOI 10.1162/coli.2007.33.4.469](https://doi.org/10.1162/coli.2007.33.4.469)
- [OpenAlex](https://openalex.org/W2075790020)

**Authors (2)**

1. [Michaela Atterer](https://openalex.org/A5031595089) [Semantic Scholar](https://www.semanticscholar.org/author/2347406) · h 8 · 535 citations · 24 works
2. [Hinrich Schütze](https://openalex.org/A5071144367) [ORCID](https://orcid.org/0000-0001-9514-7934) · [Semantic Scholar](https://www.semanticscholar.org/author/144418438) · h 65 · 51702 citations · 501 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

Most PP attachment work gives the classifier the two candidate heads from gold parse trees, which the authors call an oracle. They remove the oracle and run the Bikel parser instead. In the realistic setting, 45,156 Penn Treebank sentences train the parser and 3,097 sentences serve as the test set. Oracle-based disambiguation reaches 84.14%, against 80.01% without an oracle. The published reattachers that use gold information score 84.18% (Collins and Brooks), 84.60% (Olteanu and Moldovan) and 85.86% (Toutanova, Manning and Ng) on the RRR set. The authors conclude that evaluating PP attachment in isolation gives numbers that are too optimistic for parsing.

## Ideas

- I1. `en parse-eval`: report PP attachment accuracy taken from the predicted tree, not from gold (v, n1, p, n2) tuples, next to LAS on UD EWT test. Count the PP cases the parser misses as a separate number.
- I2. Report three figures for every PP score, as the paper does: missed cases counted as errors, missed cases given the noun attachment, and missed cases discarded. A score that rises only when cases are discarded must not pass the gate.
- I3. `en/src/parse.rs`: add the head of the PP object as a feature only when the object is a single token that the parser has tagged as a noun. The paper finds noisy head information lowers accuracy. Measure LAS on EWT test with and without the feature.

## For Mova

The chapter is [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/). The code is `en/src/parse.rs` and the command `en parse-eval`. The paper's data is Penn Treebank, so the RRR numbers are test only. Its results are for a Bikel parser and 2007 data; the first Mova measurement must be LAS and attachment accuracy from the `en` parser on EWT test.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/coli.2007.33.4.469](https://doi.org/10.1162/coli.2007.33.4.469)

```
@article{atterer2007prepositional,
  title = {{Prepositional Phrase Attachment without Oracles}},
  author = {Atterer, Michaela and Schütze, Hinrich},
  journal = {Computational Linguistics},
  year = {2007},
  doi = {10.1162/coli.2007.33.4.469},
  url = {https://doi.org/10.1162/coli.2007.33.4.469}
}
```

- [← previous The Notion of Argument in Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/cl-2006-merlo-notion-argument-prepositional-phrase-attachment/)
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- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

20 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Atterer, Michaela and Hinrich Schütze. 2006. The effect of corpus size in combining supervised and unsupervised training for disambiguation. In Proceedings of the Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING/… [find](https://scholar.google.com/scholar?q=Atterer%2C+Michaela+and+Hinrich+Schu%CC%88tze%2E+2006%2E+The+effect+of+corpus+size+in+combining+supervised+and+unsupervised+training+for+disambiguation%2E+In+Proceedings+of+the+Joint+Conference+of+the+Internationa)
2. Bharati, Akshar, U. Rohini, P. Vishnu, Sushma Bendre, and Rajeev Sangal. 2005. A hybrid approach to single and multiple PP attachment using WordNet. In Proceedings of International Joint Conference on Natural Language Processing (IJCNLP) 2005, pages 211–222, Jeju Island, Korea. [find](https://scholar.google.com/scholar?q=Bharati%2C+Akshar%2C+U%2E+Rohini%2C+P%2E+Vishnu%2C+Sushma+Bendre%2C+and+Rajeev+Sangal%2E+2005%2E+A+hybrid+approach+to+single+and+multiple+PP+attachment+using+WordNet%2E+In+Proceedings+of+International+Joint+Conference+on)
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4. Calvo, Hiram, Alexander Gelbukh, and Adam Kilgarriff. 2005. Distributional thesaurus vs. WordNet: A comparison of backoff techniques for unsupervised PP attachment. In Proceedings of the Sixth International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2005), pages… [find](https://scholar.google.com/scholar?q=Calvo%2C+Hiram%2C+Alexander+Gelbukh%2C+and+Adam+Kilgarriff%2E+2005%2E+Distributional+thesaurus+vs%2E+WordNet%3A+A+comparison+of+backoff+techniques+for+unsupervised+PP+attachment%2E+In+Proceedings+of+the+Sixth+Interna)
5. Charniak, Eugene and Mark Johnson. 2005. Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 173–180, Ann Arbor, MI. [find](https://scholar.google.com/scholar?q=Charniak%2C+Eugene+and+Mark+Johnson%2E+2005%2E+Coarse%2Dto%2Dfine+n%2Dbest+parsing+and+MaxEnt+discriminative+reranking%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-charniak-coarse-fine-n-best-parsing/)
6. Collins, Michael and James Brooks. 1995. Prepositional phrase attachment through a backed-off model. In Proceedings of the Third Workshop on Very Large Corpora, pages 27–38, Somerset, NJ. [find](https://scholar.google.com/scholar?q=Collins%2C+Michael+and+James+Brooks%2E+1995%2E+Prepositional+phrase+attachment+through+a+backed%2Doff+model%2E+In+Proceedings+of+the+Third+Workshop+on+Very+Large+Corpora%2C+pages+27%E2%80%9338%2C+Somerset%2C+NJ%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-1995-collins-prepositional-phrase-attachment-through-backed/)
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14. Pantel, Patrick and Dekang Lin. 2000. An unsupervised approach to prepositional phrase attachment using contextually similar words. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL), pages 101–108, Hong Kong, China. [find](https://scholar.google.com/scholar?q=Pantel%2C+Patrick+and+Dekang+Lin%2E+2000%2E+An+unsupervised+approach+to+prepositional+phrase+attachment+using+contextually+similar+words%2E+In+Proceedings+of+the+38th+Annual+Meeting+of+the+Association+for+Com) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2000-pantel-unsupervised-approach-prepositional-phrase-attachment/)
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17. Srinivas, Medimi and Pushpak Bhattacharyya. 2007. A flexible unsupervised PP-attachment method using semantic information. In Proceedings of Internationl Joint Conference on Artificial Intelligence (IJCAI 2007), pages 1677–1682, Hyderabad, India. [find](https://scholar.google.com/scholar?q=Srinivas%2C+Medimi+and+Pushpak+Bhattacharyya%2E+2007%2E+A+flexible+unsupervised+PP%2Dattachment+method+using+semantic+information%2E+In+Proceedings+of+Internationl+Joint+Conference+on+Artificial+Intelligence+%28I)
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