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# Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition

**Authors:** [Roy Bar-Haim](https://md.mova.elamur.ai/free/en/docs/papers/authors/roy-bar-haim/), [Ido Dagan](https://md.mova.elamur.ai/free/en/docs/papers/authors/ido-dagan/), Iddo Greental, [Idan Szpektor](https://md.mova.elamur.ai/free/en/docs/papers/authors/idan-szpektor/) and Moshe Friedman · **Year:** 2007 · **Venue:** Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing **Link:** [aclanthology.org/W07-1422](https://aclanthology.org/W07-1422/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**6** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/haim-2007-semantic-inference-lexical-syntactic-level/) of the 140673 papers whose bibliographies we read[Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing](https://aclanthology.org/volumes/W07-14/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W07-1422/)
- [PDF](https://aclanthology.org/W07-1422.pdf)

**Authors (5)**

1. Roy Bar-Haim
2. Ido Dagan
3. Iddo Greental
4. Idan Szpektor
5. Moshe Friedman

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

## What it is about

The paper proposes a proof system for textual entailment that works directly on dependency trees. Entailment rules, manual or acquired from WordNet and from DIRT-style paraphrase data, rewrite the text tree step by step towards the hypothesis, and a cost function measures the remaining distance when no full proof is found. Proofs are limited to depth four, and the parser is Minipar. On the RTE-3 test set the full system reaches 62.4% accuracy with an optimal threshold, and the lexical-only configuration 62.9%, so the richer rules give no consistent gain. On the development set exact proofs have 92% precision but only 4.3% recall; lexical-syntactic rules fire in about 3% of attempted proofs, and missing world knowledge is the main cause of failure.

## Ideas

- I1. `en/src/morph.rs` and the tree code: a small rule base in the paper's form, a left template matched on a UD tree, a right template generated, and alignment rules that copy the modifiers. Start with the passive-to-active rule and check it on tree pairs built from the school lessons.
- I2. Negation and modality as annotation rules: they set features on nodes and block later rewrites (for example under "never"). Measure how many false entailments appear when the blocking is switched off, on a small negative control set.
- I3. Report exact-proof precision and recall separately, and use a cost threshold only when no proof is found, as the paper does. This keeps every "yes" traceable to the rules that produced it.
- I4. A polarity column for complement-taking verbs: "know that p" licenses p in a positive context, "believe that p" does not. Put the list in the complementation chapter and test it on paired sentences.

## For Mova

The chapters are [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) (the passive rule), [27. Negation](https://md.mova.elamur.ai/free/en/docs/languages/en/27-negation/) (negation and polarity) and [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/) (complement polarity). The code is in `prag/src/infer.rs` and `world/src/qa.rs`. Measure exact-proof precision and recall on a held-out set of pairs, and run it twice to check determinism.

## Cite

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

```
@inproceedings{barhaim2007semantic,
  title = {{Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition}},
  author = {Bar-Haim, Roy and Dagan, Ido and Greental, Iddo and Szpektor, Idan and Friedman, Moshe},
  booktitle = {Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing},
  year = {2007},
  url = {https://aclanthology.org/W07-1422/}
}
```

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## References

10 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Roy Bar-Haim, Ido Dagan, Iddo Greental, and Eyal Shnarch. 2007. Semantic inference at the lexical-syntactic level. In AAAI (to appear). [find](https://scholar.google.com/scholar?q=Roy+Bar%2DHaim%2C+Ido+Dagan%2C+Iddo+Greental%2C+and+Eyal+Shnarch%2E+2007%2E+Semantic+inference+at+the+lexical%2Dsyntactic+level%2E+In+AAAI+%28to+appear%29%2E)
2. Christiane Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. Language, Speech and Communication. MIT Press. [find](https://scholar.google.com/scholar?q=Christiane+Fellbaum%2C+editor%2E+1998%2E+WordNet%3A+An+Electronic+Lexical+Database%2E+Language%2C+Speech+and+Communication%2E+MIT+Press%2E)
3. Dekang Lin and Patrik Pantel. 2001. Discovery of inference rules for question answering. Natural Language Engineering, 4(7):343–360. [find](https://scholar.google.com/scholar?q=Dekang+Lin+and+Patrik+Pantel%2E+2001%2E+Discovery+of+inference+rules+for+question+answering%2E+Natural+Language+Engineering%2C+4%287%29%3A343%E2%80%93360%2E)
4. Dekang Lin. 1998a. Automatic retrieval and clustering of similar words. In Proceedings of COLING/ACL. [find](https://scholar.google.com/scholar?q=Dekang+Lin%2E+1998a%2E+Automatic+retrieval+and+clustering+of+similar+words%2E+In+Proceedings+of+COLING%2FACL%2E)
5. Dekang Lin. 1998b. Dependency-based evaluation of minipar. In Proceedings of the Workshop on Evaluation of Parsing Systems at LREC. [find](https://scholar.google.com/scholar?q=Dekang+Lin%2E+1998b%2E+Dependency%2Dbased+evaluation+of+minipar%2E+In+Proceedings+of+the+Workshop+on+Evaluation+of+Parsing+Systems+at+LREC%2E)
6. C. Macleod, R. Grishman, A. Meyers, L. Barrett, and R. Reeves. 1998. Nomlex: A lexicon of nominalizations. In EURALEX. [find](https://scholar.google.com/scholar?q=C%2E+Macleod%2C+R%2E+Grishman%2C+A%2E+Meyers%2C+L%2E+Barrett%2C+and+R%2E+Reeves%2E+1998%2E+Nomlex%3A+A+lexicon+of+nominalizations%2E+In+EURALEX%2E)
7. Ruslan Mitkov, Richard Evans, and Constantin Orasan. 2002. A new, fully automatic version of Mitkov’s knowledge-poor pronoun resolution method. In Proceedings of CICLing. [find](https://scholar.google.com/scholar?q=Ruslan+Mitkov%2C+Richard+Evans%2C+and+Constantin+Orasan%2E+2002%2E+A+new%2C+fully+automatic+version+of+Mitkov%E2%80%99s+knowledge%2Dpoor+pronoun+resolution+method%2E+In+Proceedings+of+CICLing%2E)
8. Rowan Nairn, Cleo Condoravdi, and Lauri Karttunen. 2006. Computing relative polarity for textual inference. In Proceedings of ICoS-5. [find](https://scholar.google.com/scholar?q=Rowan+Nairn%2C+Cleo+Condoravdi%2C+and+Lauri+Karttunen%2E+2006%2E+Computing+relative+polarity+for+textual+inference%2E+In+Proceedings+of+ICoS%2D5%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2006-nairn-computing-relative-polarity-textual-inference/)
9. Jeffrey C. Reynar and Adwait Ratnaparkhi. 1997. A maximum entropy approach to identifying sentence boundaries. In Proceedings of ANLP. [find](https://scholar.google.com/scholar?q=Jeffrey+C%2E+Reynar+and+Adwait+Ratnaparkhi%2E+1997%2E+A+maximum+entropy+approach+to+identifying+sentence+boundaries%2E+In+Proceedings+of+ANLP%2E)
10. Tal Ron. 2006. Generating entailment rules based on online lexical resources. Master’s thesis, Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel. [find](https://scholar.google.com/scholar?q=Tal+Ron%2E+2006%2E+Generating+entailment+rules+based+on+online+lexical+resources%2E+Master%E2%80%99s+thesis%2C+Computer+Science+Department%2C+Bar%2DIlan+University%2C+Ramat%2DGan%2C+Israel%2E)