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# An Empirical Approach to VP Ellipsis

**Authors:** [Daniel Hardt](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-hardt/) · **Year:** 1997 · **Venue:** Computational Linguistics, Volume 23, Number 4 **Link:** [aclanthology.org/J97-4002](https://aclanthology.org/J97-4002/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**37** citations](https://openalex.org/works?filter=cites:W2168671000) openalex-snapshot · 2026-10-14[**17** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cl-1997-hardt-empirical-approach-vp-ellipsis/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J97-4/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J97-4002/)
- [PDF](https://aclanthology.org/J97-4002.pdf)
- [OpenAlex](https://openalex.org/W2168671000)

**Authors (1)**

1. [Hardt, Daniel](https://openalex.org/A5054824570) [ORCID](https://orcid.org/0000-0001-6041-1475) · [Semantic Scholar](https://www.semanticscholar.org/author/144610213) · h 19 · 1595 citations · 98 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Hardt builds the first corpus-based system for resolving VP ellipsis (VPE). The data are 644 VPE examples found in the parsed Penn Treebank, 260 of them from the Wall Street Journal. A syntactic filter first removes candidate antecedents in impossible configurations. The remaining candidates are ranked by four preference factors: recency, clausal relations, parallelism and quotation structure. On a blind test of 96 WSJ examples, the system gets 94.8% head-overlap success, against 75.0% for a recency baseline. Its exact-match accuracy is 76.0%, against 14.6% for the baseline. Human coders agree on the exact match in 93% of cases. Removing the clausal-relation factor drops the score from 75.9% to 72.8%.

## Ideas

- I1. `en/src/expert.rs`: write the syntactic filter as rules over UD trees. Each rule rejects a candidate verb head in a configuration where VPE cannot take it as antecedent, and the rule name is logged in `en/src/explain.rs`, so every rejection can say why.
- I2. Recency baseline first: report the recency-only score next to the ranked score on the same test set, with the head-overlap and exact-match measures from the paper. A ranker that does not beat recency by a wide margin is not worth its rules.
- I3. Factor ablation on training data only: drop each preference factor in turn, as the paper does for Clause-Rel, and report the drop with `en folds`. The test set stays held out until the release check.

## For Mova

The code is `en/src/expert.rs` and `en/src/explain.rs`. The chapter is [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/). The Penn Treebank data is licensed, so the paper's numbers are not Mova numbers and no Penn data goes into what is built. Measure first on a VPE set whose licence allows testing: check the licence before any run.

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/)

## Cite

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

```
@article{hardt1997empirical,
  title = {{An Empirical Approach to VP Ellipsis}},
  author = {Hardt, Daniel},
  journal = {Computational Linguistics},
  year = {1997},
  url = {https://aclanthology.org/J97-4002/}
}
```

- [← previous Centering: A Framework for Modeling the Local Coherence of Discourse](https://md.mova.elamur.ai/free/en/docs/papers/cl-1995-grosz-centering-framework-modeling-local-coherence/)
- [next → Head-Driven Statistical Models for Natural Language Parsing](https://md.mova.elamur.ai/free/en/docs/papers/cl-2003-collins-head-driven-statistical-models-natural/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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