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# Combining Unsupervised and Supervised Methods for PP Attachment Disambiguation

**Authors:** [Martin Volk](https://md.mova.elamur.ai/free/en/docs/papers/authors/martin-volk/) · **Year:** 2002 · **Venue:** COLING 2002: The 19th International Conference on Computational Linguistics **Link:** [aclanthology.org/C02-1004](https://aclanthology.org/C02-1004/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**23** citations](https://openalex.org/works?filter=cites:W1995235220) openalex · 2026-10-18[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2002-volk-combining-unsupervised-supervised-methods-pp/) of the 140673 papers whose bibliographies we read[COLING 2002: The 19th International Conference on Computational Linguistics](https://aclanthology.org/volumes/C02-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/C02-1004/)
- [PDF](https://aclanthology.org/C02-1004.pdf)
- [DOI 10.3115/1072228.1072232](https://doi.org/10.3115/1072228.1072232)
- [OpenAlex](https://openalex.org/W1995235220)

**Authors (1)**

1. [Volk, Martin](https://openalex.org/A5055769728) [ORCID](https://orcid.org/0000-0002-2063-4516) · h 23 · 2003 citations · 187 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 asks what to do when only a small treebank exists for prepositional phrase (PP) attachment. The data are German: 10,000 NEGRA sentences give the supervised examples, and about 5.5 million words of a computer magazine give raw co-occurrence counts. A cascade of count rules, with a noun factor of 5.47 or 5.97 to balance verb and noun counts, reaches 81.67% on the decidable cases (90.13% coverage) of a 4469-item test set. A back-off model trained on the small treebank reaches 73.98%. Ordering the two sources by reliability gives 80.98% on all 4469 items. With the roles swapped, the combination gives 71.95% against 68.29% for back-off alone.

## Ideas

- I1. `en/src/parse.rs`: add a cascade step for PP arcs (`obl`, `nmod` and `advcl` with a PP head). Each level is a count table with a threshold, and the level that decides is recorded. Measure coverage and accuracy on the EWT test PP attachments against the current perceptron choice. The tables come from EWT train only.
- I2. Verb/noun bias factor: compute the ratio of overall verb-preposition to noun-preposition co-occurrence in the training data (the paper's noun factor) and scale the noun score before comparing. Measure on the two-fold check (`en folds`), not on the test set, and run twice to confirm identical output.
- I3. Support-verb units as the first level: light-verb frames such as "take into account" or "make use of" with a PP, taken from the MWE list in [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/). Check the precision on EWT train before the rule is used. In the paper this level is very reliable (97 cases at 100% on German); the English count is unknown until measured.

## For Mova

The chapters are [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) and `23-complementation.md` (verb or noun choice for a PP complement). The code is `en/src/parse.rs`, with `en/src/explain.rs` for the per-level reason. The German figures do not carry over to English; the first English number must come from EWT test, with the two-fold check for decisions. Count tables are built from EWT train, which is CC BY-SA and may be used for training.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1072228.1072232](https://doi.org/10.3115/1072228.1072232)

```
@inproceedings{volk2002combining,
  title = {{Combining Unsupervised and Supervised Methods for PP Attachment Disambiguation}},
  author = {Volk, Martin},
  booktitle = {COLING 2002: The 19th International Conference on Computational Linguistics},
  year = {2002},
  doi = {10.3115/1072228.1072232},
  url = {https://doi.org/10.3115/1072228.1072232}
}
```

- [← previous Unsupervised discovery of morphologically related words based on orthographic and semantic similarity](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2002-baroni-unsupervised-discovery-morphologically-related-words/)
- [next → Supersense Tagging of Unknown Nouns in WordNet](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2003-ciaramita-supersense-tagging-unknown-nouns-wordnet/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

7 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. E. Brill and P. Resnik. 1994. A rule-based approach to prepositional phrase attachment disambiguation. In Proceedings of COLING, pages 1198–1204, Kyoto. ACL. [find](https://scholar.google.com/scholar?q=E%2E+Brill+and+P%2E+Resnik%2E+1994%2E+A+rule%2Dbased+approach+to+prepositional+phrase+attachment+disambiguation%2E+In+Proceedings+of+COLING%2C+pages+1198%E2%80%931204%2C+Kyoto%2E+ACL%2E)
2. M. Collins and J. Brooks. 1995. Prepositional phrase attachment through a backed-off model. In Proc. of the Third Workshop on Very Large Corpora. [find](https://scholar.google.com/scholar?q=M%2E+Collins+and+J%2E+Brooks%2E+1995%2E+Prepositional+phrase+attachment+through+a+backed%2Doff+model%2E+In+Proc%2E+of+the+Third+Workshop+on+Very+Large+Corpora%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-1995-collins-prepositional-phrase-attachment-through-backed/)
3. D. Hindle and M. Rooth. 1993. Structural ambiguity and lexical relations. Computational Linguistics, 19(1):103–120. correct incorrect accuracy 2400 469 83.65% 1219 381 76.19% 3619 850 80.98% 4469 (of 4469) coverage: 100% threshold 0.020 0.109 number 97 6 269 845 2677 71 81 202 210 11 4469 coverage 2… [find](https://scholar.google.com/scholar?q=D%2E+Hindle+and+M%2E+Rooth%2E+1993%2E+Structural+ambiguity+and+lexical+relations%2E+Computational+Linguistics%2C+19%281%29%3A103%E2%80%93120%2E+correct+incorrect+accuracy+2400+469+83%2E65%25+1219+381+76%2E19%25+3619+850+80%2E98%25+4469+%28of+)
4. Adwait Ratnaparkhi. 1998. Statistical models for unsupervised prepositional phrase attachment. In Proceedings of COLING-ACL-98, Montreal. [find](https://scholar.google.com/scholar?q=Adwait+Ratnaparkhi%2E+1998%2E+Statistical+models+for+unsupervised+prepositional+phrase+attachment%2E+In+Proceedings+of+COLING%2DACL%2D98%2C+Montreal%2E)
5. W. Skut, T. Brants, B. Krenn, and H. Uszkoreit. 1998. A linguistically interpreted corpus of German newspaper text. In Proc. of ESSLLI-98 Workshop on Recent Advances in Corpus Annotation, Saarbrücken. [find](https://scholar.google.com/scholar?q=W%2E+Skut%2C+T%2E+Brants%2C+B%2E+Krenn%2C+and+H%2E+Uszkoreit%2E+1998%2E+A+linguistically+interpreted+corpus+of+German+newspaper+text%2E+In+Proc%2E+of+ESSLLI%2D98+Workshop+on+Recent+Advances+in+Corpus+Annotation%2C+Saarbru%CC%88cken)
6. J. Stetina and M. Nagao. 1997. Corpus based PP attachment ambiguity resolution with a semantic dictionary. In J. Zhou and K. Church, editors, Proc. of the 5th Workshop on Very Large Corpora, Beijing and Hongkong. [find](https://scholar.google.com/scholar?q=J%2E+Stetina+and+M%2E+Nagao%2E+1997%2E+Corpus+based+PP+attachment+ambiguity+resolution+with+a+semantic+dictionary%2E+In+J%2E+Zhou+and+K%2E+Church%2C+editors%2C+Proc%2E+of+the+5th+Workshop+on+Very+Large+Corpora%2C+Beijing+a)
7. Martin Volk. 2001. Exploiting the WWW as a corpus to resolve PP attachment ambiguities. In Proc. of Corpus Linguistics 2001, Lancaster, March. [find](https://scholar.google.com/scholar?q=Martin+Volk%2E+2001%2E+Exploiting+the+WWW+as+a+corpus+to+resolve+PP+attachment+ambiguities%2E+In+Proc%2E+of+Corpus+Linguistics+2001%2C+Lancaster%2C+March%2E)