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# Using Syntax to Resolve NPE in English

**Authors:** [Payal Khullar](https://md.mova.elamur.ai/free/en/docs/papers/authors/payal-khullar/), Allen Antony and [Manish Shrivastava](https://md.mova.elamur.ai/free/en/docs/papers/authors/manish-shrivastava/) · **Year:** 2019 · **Venue:** Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) **Link:** [aclanthology.org/R19-1063](https://aclanthology.org/R19-1063/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**8** citations](https://openalex.org/works?filter=cites:W2990799060) openalex · 2026-10-18[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2019-khullar-syntax-resolve-npe-english/) of the 140673 papers whose bibliographies we read[Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)](https://aclanthology.org/volumes/R19-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/R19-1063/)
- [PDF](https://aclanthology.org/R19-1063.pdf)
- [DOI 10.26615/978-954-452-056-4_063](https://doi.org/10.26615/978-954-452-056-4_063)
- [OpenAlex](https://openalex.org/W2990799060)

**Authors (3)**

1. [Payal Khullar](https://openalex.org/A5006065835) h 3 · 47 citations · 7 works
2. [Allen Antony](https://openalex.org/A5051992586) h 2 · 18 citations · 11 works
3. [Manish Shrivastava](https://openalex.org/A5013675579) [ORCID](https://orcid.org/0000-0001-8705-6637) · h 28 · 2469 citations · 226 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 describes a rule-based system for noun phrase ellipsis (NPE), where the head noun is omitted, as in "We are all partying at Mary's [e]". The system takes free English text, finds the ellipsis site, and then picks antecedent candidates. The rules follow syntactic constraints on NPE licensors, such as cardinal numbers, genitives and adjectives, and the parallelism between antecedent and ellipsis contexts. The authors curated a set of 337 sentences: 205 positive and 132 negative. They tuned the rules on 140 positive sentences, and tested on 65 further positive sentences (76 NPE instances) and the 132 negatives. Detection reaches an F1 of 76.47% (precision 69.15%, recall 85.53%). Resolution reaches an F1 of 70.27%.

## Ideas

- I1. `en/src/expert.rs`: encode the licensor rules as UD patterns. A numeral, determiner or adjective with no nominal head, or a genitive with no head, is a candidate NPE site. Report precision and recall for each licensor type separately, so that a weak licensor shows up.
- I2. Head-less detection on EWT: count the candidate sites that the licensor rules find on EWT test, and check 50 by hand. The count shows how often the rule fires on ordinary text.
- I3. Dataset: the authors say the 337-sentence set will be made public. Before any use, check its licence. If it is open, use it as a test set only, never to tune the rules that are then measured on it.

## For Mova

The code is `en/src/expert.rs`, and the chapter is [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) (NP structure) with [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/). The dataset is small and was curated by hand, so the numbers are not a benchmark. Measure first: the licensor rules on EWT test, with the hand check of 50 hits, before tuning anything.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.26615/978-954-452-056-4_063](https://doi.org/10.26615/978-954-452-056-4_063)

```
@inproceedings{khullar2019using,
  title = {{Using Syntax to Resolve NPE in English}},
  author = {Khullar, Payal and Antony, Allen and Shrivastava, Manish},
  booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)},
  year = {2019},
  doi = {10.26615/978-954-452-056-4\_063},
  url = {https://doi.org/10.26615/978-954-452-056-4_063}
}
```

- [← previous Few-Shot and Zero-Shot Learning for Historical Text Normalization](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2019-bollmann-few-shot-zero-shot-learning/)
- [next → Sentence Boundary Detection on Line Breaks in Japanese](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-hayashibe-sentence-boundary-detection-line-breaks/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

40 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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