---
title: "On Improving the Accuracy of Readability — Vajjala 2012"
description: "The paper addresses automatic readability classification: assigning a school grade level to a text so that teachers, language learners and readers with…"
authors: ["Sowmya Vajjala", "Detmar Meurers"]
year: 2012
venue: "Proceedings of the Seventh Workshop on Building Educational Applications Using NLP"
source: "https://aclanthology.org/W12-2019/"
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/vajjala-2012-improving-accuracy-readability-classification-insights/"
---
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# On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition

**Authors:** [Sowmya Vajjala](https://md.research.elamur.ai/free/en/authors/sowmya-vajjala/), [Detmar Meurers](https://md.research.elamur.ai/free/en/authors/detmar-meurers/) · **Year:** 2012 · **Venue:** Proceedings of the Seventh Workshop on Building Educational Applications Using NLP · **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/W12-2019/) · [PDF](https://aclanthology.org/W12-2019.pdf) · [OpenAlex](https://openalex.org/W33677238)

**Citations:** 194 citations (openalex-snapshot · 2026-10-14) · [117](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses automatic readability classification: assigning a school grade level to a text so that teachers, language learners and readers with special needs can find suitable material, for example through a language-aware search engine. The authors build WeeBit, a new five-level graded corpus. It merges WeeklyReader levels (ages 7–10) with BBC Bitesize KS3 and GCSE pages (ages 11–16), and overlapping levels are dropped so the combined corpus spans ages 7 to 16. Their central idea is to reuse measures from Second Language Acquisition research as classifier features. These measures were originally designed to track lexical richness and syntactic complexity in learners' own writing. The authors combine them with classic surface cues such as word and sentence length and with parse-based features. The full model reaches 93.3% accuracy on WeeBit. When restricted to WeeklyReader, it clearly outperforms earlier published systems. The work shows that measures of language development in learners also capture how hard a text is to comprehend.

## Key points
- WeeBit contains five classes: WeeklyReader Level 2, Level 3 and Level 4, plus Bitesize KS3 and GCSE. Each class is balanced to 500 training and 125 test documents, giving 2,500 training and 625 test documents in total.
- Used alone, the SLA-derived lexical features reached 68.1% accuracy and the SLA syntactic features 71.2%. Combining the two raised accuracy to 82.3%, and the full feature set achieved 93.3%.
- On the four-level WeeklyReader data alone, 46 features gave 91.3% accuracy. Feng et al. (2010) had reported 74.01% with 122 features.
- With training sizes matched to earlier studies, the model scored 84.2% on 1,400 documents (Feng et al.: 74.01%). On 2,500 documents it scored 88.4% (Petersen and Ostendorf: 63.18%).
- A re-implementation of the Schwarm and Ostendorf (2005) parse features gave 50.7%, compared with their published 50.91%. This supports the claim that the newly downloaded WeeklyReader data is comparable to the data used in earlier work.

## Interesting ideas
- **Borrowing production metrics for comprehension tasks.** Measures built to score how complex learners' own output is can double as predictors of how difficult a text is to read. Other fields' developmental metrics, for example from child language studies, could likewise become cheap features for difficulty estimation.
- **Joining graded sources and cutting overlaps.** Two independently graded collections are merged into one scale, and any levels whose age ranges overlap are discarded. This widens coverage while keeping class labels clean, a recipe that applies whenever several leveled resources exist for a language.
- **Balancing classes by sampling.** Drawing an equal number of documents per level for training and testing keeps the classifier from favouring the largest class. This matters here because GCSE alone has 3,500 articles.
- **Checking comparability when the old data is unavailable.** The authors re-implement a prior feature set and confirm they reproduce its published accuracy on their data. They also rebuild training sets of the sizes used in earlier studies. Together these give a credible comparison even when the original datasets cannot be obtained.
- **Guarding against formula circularity.** The authors note that the graded texts may have been written with traditional readability formulae in mind, which could inflate the value of surface features. Removing those features to test whether the other features still carry the signal is a useful robustness check for any benchmark labelled by humans who may have followed known heuristics.

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

## Cited by

Cited by

117 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 110 not yet.

- [KoKo: an L1 Learner Corpus for German](https://md.research.elamur.ai/free/en/papers/abel-2014-koko-l1-learner-corpus-german/)
- [Modélisation de la lisibilité en français pour les personnes en situation d'illettrisme](https://md.research.elamur.ai/free/en/papers/aissa-2025-modelisation-de-la-lisibilite-en/)
- [Supervised and Unsupervised Neural Approaches to Text Readability](https://md.research.elamur.ai/free/en/papers/martinc-2021-supervised-unsupervised-neural-approaches-text/)
- [Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/)
- [OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification](https://md.research.elamur.ai/free/en/papers/vajjala-2018-onestopenglish-corpus-new-corpus-automatic/)
- [Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty](https://md.research.elamur.ai/free/en/papers/wu-2026-translation-as-augmentation-effect-translated/)
- [Text Readability Assessment for Second Language Learners](https://md.research.elamur.ai/free/en/papers/xia-2016-text-readability-assessment-second-language/)

## Cite

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

```bibtex
@inproceedings{vajjala2012improving,
  title = {{On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition}},
  author = {Vajjala, Sowmya and Meurers, Detmar},
  booktitle = {Proceedings of the Seventh Workshop on Building Educational Applications Using NLP},
  year = {2012},
  url = {https://aclanthology.org/W12-2019/}
}
```

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37 references, 5 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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