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# Effective Analysis of Causes and Inter-dependencies of Parsing Errors

**Authors:** [Tadayoshi Hara](https://md.mova.elamur.ai/free/en/docs/papers/authors/tadayoshi-hara/), [Yusuke Miyao](https://md.mova.elamur.ai/free/en/docs/papers/authors/yusuke-miyao/) and [Jun'ichi Tsujii](https://md.mova.elamur.ai/free/en/docs/papers/authors/jun-ichi-tsujii/) · **Year:** 2009 · **Venue:** Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09) **Link:** [aclanthology.org/W09-3828](https://aclanthology.org/W09-3828/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hara-2009-effective-analysis-causes-inter-dependencies/) of the 140673 papers whose bibliographies we read[Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)](https://aclanthology.org/volumes/W09-38/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W09-3828/)
- [PDF](https://aclanthology.org/W09-3828.pdf)

**Authors (3)**

1. Tadayoshi Hara
2. Yusuke Miyao
3. Jun'ichi Tsujii

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

## What it is about

F-scores hide where a parser fails and how its errors depend on each other. The authors analyse errors of the Enju HPSG parser on predicate-argument triplets in two ways. First, 14 hand-written pattern rules map sets of mismatched triplets to cause categories such as PP-attachment, coordination, relative-clause attachment and the infinitive as adjunct or complement. Second, each erroneous decision is forcibly corrected and the sentence is re-parsed; errors that disappear with it are treated as dependent on it, giving groups of inter-dependent errors and a propagation network. On Penn Treebank Section 0 the parser makes 4,709 triplet errors (F-score 90.69). The rules cover 2,078 of them (over 40%), and 3,085 are correctable. The re-parse analysis finds 1,978 groups and 501 propagation links. In a domain adaptation study on GENIA, the adapted model lowers the error count from 3,120 to 2,229 and the F-score rises from 87.41 to 90.93.

## Ideas

- I1. Pattern rules over dependency arcs: write the paper's cause categories (PP-attachment, relative clause, coordination scope, infinitive as complement) as rules over `en/src/parse.rs` output, and count the hits on the school texts. Each rule should say why it fired, which fits Mova's explainable style.
- I2. Forced-correction re-parse as a test: for each wrong arc in a tree, fix it and re-run the parser; record which other arcs change. Build the propagation graph in a BTreeMap and use it to order the fixes.
- I3. Coverage report by category: like the paper's Table 3, report how many errors each rule explains and how many remain unclassified, so each new rule is measured on held-out lessons.
- I4. Negative control: apply the same rules to a shuffled or corrupted parse. The rule hit rate must drop sharply, or the rules are not measuring real errors.
- I5. Domain check: run `en parse-eval` separately on the school questions and the narrative texts, and report the category distribution per domain, as the paper does for PTB and GENIA.

## For Mova

The cause categories map to [25. Relative clauses](https://md.mova.elamur.ai/free/en/docs/languages/en/25-relative-clauses/), [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/) (infinitive and gerund), and [24. Coordination and subordination](https://md.mova.elamur.ai/free/en/docs/languages/en/24-coordination-subordination/). Code: `en/src/parse.rs` for the arcs, a new error-analysis step in `tools/parsecmp` for the categories and the re-parse, and `en parse-eval` as the base measure. Measure: the share of parser errors covered by rules, the number of propagation links, and the held-out UAS on UD EWT dev, run twice to confirm determinism.

## Cite

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

```
@inproceedings{hara2009effective,
  title = {{Effective Analysis of Causes and Inter-dependencies of Parsing Errors}},
  author = {Hara, Tadayoshi and Miyao, Yusuke and Tsujii, Jun'ichi},
  booktitle = {Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)},
  year = {2009},
  url = {https://aclanthology.org/W09-3828/}
}
```

- [← previous Uniform Information Density and Syntactic Reduction: Revisiting that-Mentioning in English Complement Clauses](https://md.mova.elamur.ai/free/en/docs/papers/hao-2025-uniform-information-density-syntactic-reduction-revisiting/)
- [next → Exploring Difficulties in Parsing Imperatives and Questions](https://md.mova.elamur.ai/free/en/docs/papers/hara-2011-exploring-difficulties-parsing-imperatives-questions/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

11 references, 5 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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2. Jesús Giménez and Lluı́s Màrquez. 2008. Towards heterogeneous automatic mt error analysis. In Proceedings of the Sixth International Language Resources and Evaluation (LREC’08), pages 1894–1901. [find](https://scholar.google.com/scholar?q=Jesu%CC%81s+Gime%CC%81nez+and+Llu%C4%B1%CC%81s+Ma%CC%80rquez%2E+2008%2E+Towards+heterogeneous+automatic+mt+error+analysis%2E+In+Proceedings+of+the+Sixth+International+Language+Resources+and+Evaluation+%28LREC%E2%80%9908%29%2C+pages+1894%E2%80%931901%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/gimenez-2008-heterogeneous-automatic-mt-error-analysis/)
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7. Ryan McDonald and Joakim Nivre. 2007. Characterizing the errors of data-driven dependency parsing models. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 122–131. [find](https://scholar.google.com/scholar?q=Ryan+McDonald+and+Joakim+Nivre%2E+2007%2E+Characterizing+the+errors+of+data%2Ddriven+dependency+parsing+models%2E+In+Proceedings+of+the+2007+Joint+Conference+on+Empirical+Methods+in+Natural+Language+Processin) [our card](https://md.mova.elamur.ai/free/en/docs/papers/missing4-2007-mcdonald-characterizing-errors-data-driven-dependency/)
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