---
title: "An “AI readability” Formula for French as a — François 2012"
description: "Readability tools for French as a foreign language (FFL) have been scarce, and the few that exist either use only a handful of features or target one…"
authors: ["Thomas François", "Cédrick Fairon"]
year: 2012
venue: "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning"
source: "https://aclanthology.org/D12-1043/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/francois-2012-ai-readability-formula-french-as/"
---
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# An “AI readability” Formula for French as a Foreign Language

**Authors:** [Thomas François](https://md.research.elamur.ai/free/en/authors/thomas-francois/), Cédrick Fairon · **Year:** 2012 · **Venue:** Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D12-1043/) · [PDF](https://aclanthology.org/D12-1043.pdf) · [OpenAlex](https://openalex.org/W2150783756)

**Citations:** 109 citations (openalex-snapshot · 2026-10-14) · [65](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Readability tools for French as a foreign language (FFL) have been scarce, and the few that exist either use only a handful of features or target one learner group, such as English speakers. The authors build a generic "AI readability" model that assigns a text to one of the six CEFR levels (A1–C2). They compiled a corpus from FFL textbooks, labelling each text with its textbook's level. Textbooks whose labelling looked inconsistent were removed, leaving 1,852 texts. Each text was described by a large set of candidate predictors, grouped as lexical, syntactic, semantic and FFL-specific. The authors then compared several feature-selection strategies and learning algorithms.

The best system is an SVM using 46 features. It beats the previous generic FFL model, François (2009), by about 8 accuracy points, and the difference is statistically significant. A surprising finding is that semantic features add almost nothing beyond lexico-syntactic ones. This challenges a common assumption from L1 readability research and suggests that L2 difficulty is driven mainly by vocabulary and syntax.

## Key points
- **Corpus:** 28 FFL textbooks published after 2001 supplied 2,160 reading-comprehension texts. After 5 task-oriented textbooks (249 texts) were dropped, the final set has 1,852 texts and about 510k tokens. The largest level is B1 with 552 texts and the smallest is C2 with 108.
- **Features:** 406 candidate features were computed. Word frequencies came from Lexique3, a subtitle-based lexicon covering lemmas and inflected forms. Out-of-list word rates used the Gougenheim et al. list and the Alter Ego textbook list.
- **Main result:** The final SVM model uses 46 features. It gains 8 accuracy points over François (2009), which reached 41% accuracy and 72.7% adjacent accuracy under 10-fold cross-validation (t(9) = 3.72, p = 0.002).
- **Weaker baselines:** An older classic formula scored only R = 0.55 and 33% accuracy on the same test data. Uitdenbogerd (2005) reported R = 0.87 against this model's 0.73, but that figure probably comes from training data and a small set of novel openings.
- **Semantic features:** None was chosen by automatic selection for the logistic models, and removing them did not significantly hurt the SVM. The LSA-based feature avLocalLsa-Lem was nonetheless the third-strongest predictor when used alone.

## Interesting ideas
- **Checking expert labels before training:** The authors ran ANOVA on two simple surface measures (words per sentence, letters per word) to spot sources whose labels disagree with the rest, then removed those sources. This is a cheap sanity check for any corpus labelled by many publishers or annotators.
- **Richer frequency statistics:** Instead of only averaging word frequency, they also used the median, interquartile range, and 75th/90th percentiles, both overall and per part of speech. The upper percentiles capture the rare words that actually block a reader, which could be useful for difficulty or complexity scoring in other languages.
- **Lemma and form frequencies together:** For a morphologically rich language like French, they computed frequencies for both lemmas and inflected forms. This is directly relevant to Ukrainian, whose morphology is similarly rich.
- **Small models as a baseline:** A model with only four lexico-syntactic features matched a much larger feature set. Testing compact feature sets first can save engineering effort and shows which signals really matter.
- **Ablation by feature family:** Dropping a whole family, here the semantic features, and testing whether performance changes significantly is a clean way to show whether a theoretically attractive feature type adds new information or merely duplicates existing features.

*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/) · [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

65 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 63 not yet.

- [Neural versus Phrase-Based Machine Translation Quality: a Case Study](https://md.research.elamur.ai/free/en/papers/bentivogli-2016-neural-versus-phrase-based-machine/)
- [Becoming Linguistically Mature: Modeling English and German Children's Writing Development Across School Grades](https://md.research.elamur.ai/free/en/papers/kerz-2020-becoming-linguistically-mature-modeling-english/)

## Cite

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

```bibtex
@inproceedings{francois2012ai,
  title = {{An “AI readability” Formula for French as a Foreign Language}},
  author = {François, Thomas and Fairon, Cédrick},
  booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  year = {2012},
  url = {https://aclanthology.org/D12-1043/}
}
```

- [← previous An Accurate Arabic Root-Based Lemmatizer for Information Retrieval Purposes](https://md.research.elamur.ai/free/en/papers/langstream-2012-el-shishtawy-accurate-arabic-root-based-lemmatizer/)
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