---
title: "A Survey of Paraphrasing and Textual — Androutsopoulos 2009"
description: "This JAIR survey (2010) brings together computational work on two closely related problems."
authors: ["Ion Androutsopoulos", "Prodromos Malakasiotis"]
year: 2009
venue: "I. Androutsopoulos and P. Malakasiotis, \"A Survey of Paraphrasing and Textual Entailment Methods\". Journal of Artificial Intelligence Research, 38:135-187, 2010"
source: "https://arxiv.org/abs/0912.3747"
doi: "10.1613/jair.2985"
arxiv: "0912.3747"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/androutsopoulos-2009-survey-paraphrasing-textual-entailment-methods/"
---
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# A Survey of Paraphrasing and Textual Entailment Methods

**Authors:** [Ion Androutsopoulos](https://md.research.elamur.ai/free/en/authors/ion-androutsopoulos/), [Prodromos Malakasiotis](https://md.research.elamur.ai/free/en/authors/prodromos-malakasiotis/) · **Year:** 2009 · **Venue:** I. Androutsopoulos and P. Malakasiotis, "A Survey of Paraphrasing and Textual Entailment Methods". Journal of Artificial Intelligence Research, 38:135-187, 2010 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://doi.org/10.1613/jair.2985) · [PDF](https://arxiv.org/pdf/0912.3747) · [DOI 10.1613/jair.2985](https://doi.org/10.1613/jair.2985) · [arXiv 0912.3747](https://arxiv.org/abs/0912.3747) · [OpenAlex](https://openalex.org/W2145815109)

**Citations:** 178 citations (openalex-snapshot · 2026-10-14) · [102](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This JAIR survey (2010) brings together computational work on two closely related problems. Paraphrasing covers expressions that carry roughly the same information. Textual entailment covers pairs ⟨T, H⟩ where a reader who trusts T would most likely accept H as true. The authors treat paraphrase as entailment holding in both directions, which lets them describe both fields in one framework. The survey is organised by task type: recognition, generation and extraction.

Along the way it covers the main ideas in the field:
- logic-based inference
- vector space models and the distributional hypothesis
- surface, syntactic and symbolic similarity measures
- machine learning classifiers
- decoding with transformation sequences
- word and sentence alignment
- pivot languages and bootstrapping
- synchronous grammar rules

It also lists the resources each approach usually needs and points to datasets, tools and rule collections. The survey proposes no new system. Its value is that it maps the methods to applications such as QA, summarization, information extraction, generation and machine translation. This makes it a practical starting point for anyone building inference or meaning-equivalence components.

## Key points
- Entailment between templates (e.g., "X painted Y" vs. "Y is the work of X") can be one-directional when it must hold for every possible slot filler.
- Judging a pair as a paraphrase or entailment depends on the chosen reading (e.g., the sense of "bank"). The authors suggest a lenient criterion: a pair counts as correct if some plausible reading makes it valid.
- Benchmarks and corpora mentioned include:
  - the RTE challenges (PASCAL, later NIST TAC)
  - the MSR Paraphrase Corpus
  - FATE (RTE-2 with FrameNet annotations)
  - the Multiple-Translation Chinese Corpus
  - Cohn et al.'s word-aligned paraphrase corpus
  - the Written News Compression Corpus
- Rule and lexical resources listed include DIRT, TEASE, Callison-Burch's pivot-based paraphrase rules, Zhao et al.'s POS-slotted rules, WordNet, Extended WordNet, FrameNet, VerbNet and Nomlex.
- Table 4 pairs each family of methods with the resources it requires. For example, pivot methods need multilingual parallel corpora, while distributional methods need a large monolingual corpus.

## Interesting ideas
- **QA through entailment checking:** replace the question word with each person-name entity found in a retrieved passage, then test which resulting statement the passage entails. This turns answer selection into a standard entailment classification problem.
- **Entailed rewrites for unseen MT input:** when a source phrase never appeared in training, swap it for a phrase it entails, then translate. Some meaning is lost, but the output may still beat a direct translation. The same trick could help any low-resource model facing rare constructions.
- **Redundancy filtering in summarization:** while extracting sentences, drop any candidate that paraphrases or is entailed by an already chosen sentence. This is a principled way to avoid repeating content in multi-document summaries.
- **Compression as entailment generation:** treat sentence compression as producing shorter, grammatical sentences entailed by the original, then rank the candidates by space saved and information kept.
- **Expanding extraction patterns:** grow information-extraction patterns automatically by adding paraphrases of a seed pattern, or patterns that entail it. This raises recall without hand-writing every variant.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/)

## Cited by

Cited by

102 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 99 not yet.

- [Putting it Simply: a Context-Aware Approach to Lexical Simplification](https://md.research.elamur.ai/free/en/papers/biran-2011-putting-it-simply-context-aware/)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
- [From Paraphrase Database to Compositional Paraphrase Model and Back](https://md.research.elamur.ai/free/en/papers/wieting-2015-paraphrase-database-compositional-paraphrase-model/)

## Cite

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

```bibtex
@article{androutsopoulos2009survey,
  title = {{A Survey of Paraphrasing and Textual Entailment Methods}},
  author = {Androutsopoulos, Ion and Malakasiotis, Prodromos},
  journal = {I. Androutsopoulos and P. Malakasiotis, "A Survey of Paraphrasing and Textual Entailment Methods". Journal of Artificial Intelligence Research, 38:135-187, 2010},
  year = {2009},
  doi = {10.1613/jair.2985},
  eprint = {0912.3747},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1613/jair.2985}
}
```

- [← previous A Probabilistic Model for Associative Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/sasano-2009-probabilistic-model-associative-anaphora-resolution/)
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- [↑ back to papers of 2009](https://md.research.elamur.ai/free/en/papers/2009/)

## References

230 references, 30 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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