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# Exploring Difficulties in Parsing Imperatives and Questions

**Authors:** [Tadayoshi Hara](https://md.mova.elamur.ai/free/en/docs/papers/authors/tadayoshi-hara/), [Takuya Matsuzaki](https://md.mova.elamur.ai/free/en/docs/papers/authors/takuya-matsuzaki/), [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:** 2011 · **Venue:** Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP 2011) **Link:** [aclanthology.org/I11-1084](https://aclanthology.org/I11-1084/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hara-2011-exploring-difficulties-parsing-imperatives-questions/) of the 140673 papers whose bibliographies we read[Proceedings of 5th International Joint Conference on Natural Language Processing](https://aclanthology.org/volumes/I11-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/I11-1084/)
- [PDF](https://aclanthology.org/I11-1084.pdf)

**Authors (4)**

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

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

## What it is about

Parsers trained on the Wall Street Journal fail on imperatives and questions even when the words are known, because the word order and the missing subject change the structure. The authors extract 750 imperatives and about 1,240 questions from the fiction part of the Brown Corpus of the Penn Treebank, and compare them with 3,859 questions from QuestionBank. They test two dependency parsers (MST, Malt), a deep HPSG parser (Enju) and a POS tagger, each adapted by a simple supervised method. Adaptation helps: 2 to 8 points on the Brown sets and 25 to 35 points on QuestionBank. Error analysis gives three causes: sentence-initial verbs tagged as nouns, a name used as a vocative taking the ROOT role, and a fronted to-infinitive clause that makes the parser choose the wrong main verb. For the Malt parser, ROOT errors on imperatives fall from 33 to 9 per 100 sentences after adaptation.

## Ideas

- I1. Tagger rule for the first word: in `en/src/ptag.rs`, a sentence-initial bare verb with no subject is tagged as a verb unless a determiner follows. The paper's most common tagger error is exactly this, so measure the error count per school-text lesson before and after.
- I2. Vocative rule in `en/src/parse.rs`: a name or address term set off by commas at the start or end of a sentence gets a `vocative` role and is never the root. Measure ROOT accuracy on imperative lessons, with the school questions as a held-out set.
- I3. Partition before parsing: a fronted "To X, do Y" or "If X, ..." is split off as its own unit, so the main imperative clause is parsed alone. Try it as a rule in the parser front end, and use a negative control in which the same clause is kept whole; the split should win only where it is marked.
- I4. Question-type table: label each question as wh, yes/no or tag in `prag`, and report accuracy per type. The paper shows yes/no questions are under-learned from QA-style data, so check the balance in the school question set.
- I5. Punctuation: the paper notes that quotation marks and "?" or "!" placed outside the quotes confuse the root choice. Add the two placement patterns to [07. Punctuation, capital letters, abbreviations and numbers](https://md.mova.elamur.ai/free/en/docs/languages/en/07-punctuation-capitals-numbers/) and test them with the tokenizer `en/src/tok.rs`.

## For Mova

Imperatives and questions: [26. Questions](https://md.mova.elamur.ai/free/en/docs/languages/en/26-questions/) and [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/). Vocatives and dialogue: [38. Dialogue, discourse markers and reported speech](https://md.mova.elamur.ai/free/en/docs/languages/en/38-dialogue-and-discourse-markers/). Punctuation: [07. Punctuation, capital letters, abbreviations and numbers](https://md.mova.elamur.ai/free/en/docs/languages/en/07-punctuation-capitals-numbers/). Code: `en/src/ptag.rs` (first-word tagging), `en/src/parse.rs` (vocative, ROOT choice), `en/src/tok.rs` (quotes and punctuation), `prag` for the speech-act label. Measure: tagging accuracy and ROOT accuracy on a held-out set of imperatives and questions, split by lesson, run twice for determinism.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

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

```
@inproceedings{hara2011exploring,
  title = {{Exploring Difficulties in Parsing Imperatives and Questions}},
  author = {Hara, Tadayoshi and Matsuzaki, Takuya and Miyao, Yusuke and Tsujii, Jun'ichi},
  booktitle = {Proceedings of 5th International Joint Conference on Natural Language Processing},
  year = {2011},
  url = {https://aclanthology.org/I11-1084/}
}
```

- [← previous Effective Analysis of Causes and Inter-dependencies of Parsing Errors](https://md.mova.elamur.ai/free/en/docs/papers/hara-2009-effective-analysis-causes-inter-dependencies/)
- [next → A Quantitative Model of Word Order and Movement in English, Dutch and German Complement Constructions](https://md.mova.elamur.ai/free/en/docs/papers/harbusch-2002-quantitative-model-word-order-movement/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

21 references, 6 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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