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# Recognising Textual Entailment with Logical Inference

**Authors:** [Johan Bos](https://md.mova.elamur.ai/free/en/docs/papers/authors/johan-bos/) and [Katja Markert](https://md.mova.elamur.ai/free/en/docs/papers/authors/katja-markert/) · **Year:** 2005 · **Venue:** Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), pp. 628–635 **Link:** [aclanthology.org/H05-1079](https://aclanthology.org/H05-1079/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only. The RTE data the paper uses is not licensed in this card; treat it as evaluation only.

[**229** citations](https://openalex.org/works?filter=cites:W2045254372) openalex · 2026-10-19[**82** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bos-2005-recognising-textual-entailment-logical-inference/) of the 140673 papers whose bibliographies we read[Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/H05-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/H05-1079/)
- [PDF](https://aclanthology.org/H05-1079.pdf)
- [DOI 10.3115/1220575.1220654](https://doi.org/10.3115/1220575.1220654)
- [OpenAlex](https://openalex.org/W2045254372)

**Authors (2)**

1. [Johan Bos](https://openalex.org/A5002288369) [ORCID](https://orcid.org/0000-0002-9079-5438) · [Semantic Scholar](https://www.semanticscholar.org/author/3461596) · h 36 · 4636 citations · 187 works
2. [Katja Markert](https://openalex.org/A5082971401) [Semantic Scholar](https://www.semanticscholar.org/author/1686341) · h 29 · 2775 citations · 127 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper compares two domain-independent methods for recognising textual entailment on the RTE challenge data (567 development and 800 test pairs). The shallow method uses word overlap extended by WordNet synonyms and derivations, weighted by inverse document frequency. The deep method parses text and hypothesis with a CCG parser into discourse representations, translates them into first-order logic, and asks a theorem prover (Vampire) and a model builder (Paradox), with background axioms from WordNet and the CIA Factbook. Entailment is approximated by how much the model's domain grows when the hypothesis is added. A decision tree combines the features. On the test set the shallow method reaches 56.9%, the prover alone about 52%, the deep method with model building 56.2%, and the hybrid with the RTE task type as a feature 61.2%. The prover finds only 30 proofs, so its recall is 0.058 at precision 0.767; missing background knowledge is the main cause.

## Ideas

- I1. Domain-size difference as a counting feature. Count the entities in the world built from the text and from text plus hypothesis; the absolute and relative growth are numbers Mova can explain ("the hypothesis adds two entities"). Test as a decision-list feature on the development set, run twice.
- I2. Task type as a feature. The paper gains about three points by adding the subset of the RTE data as input. A Mova rule combiner can take the lesson type the same way, chosen on cross-validation, with the test set touched once.
- I3. Explicit lexical rules: hyponym pairs entail, sister terms contradict. Keep them as a small lexicon in `en/src/dict.rs`, and log each rule that fires.
- I4. Report precision and recall of the proof route separately, use the proof only when it fires, and let the approximation handle the rest.

## For Mova

Chapter: [27. Negation](https://md.mova.elamur.ai/free/en/docs/languages/en/27-negation/) (negated hypotheses, the paper's Example 78 type). Code: `prag/src/infer.rs` for the decision, `en/src/dict.rs` for the lexical relations, `world/src/qa.rs` for the school questions. Measure: accuracy by route (proof, approximation, both), run twice for identical output.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1220575.1220654](https://doi.org/10.3115/1220575.1220654)

```
@inproceedings{bos2005recognising,
  title = {{Recognising Textual Entailment with Logical Inference}},
  author = {Bos, Johan and Markert, Katja},
  booktitle = {Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing},
  year = {2005},
  doi = {10.3115/1220575.1220654},
  url = {https://doi.org/10.3115/1220575.1220654}
}
```

- [← previous Presupposition & VP-Ellipsis](https://md.mova.elamur.ai/free/en/docs/papers/bos-1994-presupposition-vp-ellipsis/)
- [next → The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.mova.elamur.ai/free/en/docs/papers/braud-2023-disrpt-2023-shared-task-elementary/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

15 references, 0 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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