---
title: "Pushing on Text Readability Assessment: A Transformer Meets"
description: "First, it proposes three new \"advanced semantics\" features… 99% accuracy on OneStopEnglish. 95 references, cited by 67 papers here."
authors: ["Bruce W. Lee", "Yoo Sung Jang", "Jason Lee"]
year: 2021
venue: "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2021.emnlp-main.834/"
doi: "10.18653/v1/2021.emnlp-main.834"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lee-2021-pushing-text-readability-assessment-transformer/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lee-2021-pushing-text-readability-assessment-transformer/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) / Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features

# Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features

**Authors:** [Bruce W. Lee](https://md.research.elamur.ai/free/en/authors/bruce-w-lee/), Yoo Sung Jang and [Jason Lee](https://md.research.elamur.ai/free/en/authors/jason-lee/) · **Year:** 2021 · **Venue:** [Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2021](https://md.research.elamur.ai/free/en/venues/emnlp/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.emnlp-main.834/) · [PDF](https://aclanthology.org/2021.emnlp-main.834.pdf) · [DOI 10.18653/v1/2021.emnlp-main.834](https://doi.org/10.18653/v1/2021.emnlp-main.834) · [OpenAlex](https://openalex.org/W3202780679)

**Citations:** 48 citations (OpenAlex, 2026-10-14) · [67](https://md.research.elamur.ai/free/en/papers/lee-2021-pushing-text-readability-assessment-transformer/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The authors ask whether neural and feature-engineering approaches to readability assessment (RA) can be combined. Historically, RA systems have mostly been SVMs over handcrafted features, and transformers have struggled on the small corpora that dominate the field. The paper does three things. First, it proposes three new "advanced semantics" features: semantic richness, clarity, and noise. These are computed from the shape of LDA document-topic probability distributions, not from topic identities. Second, it releases LingFeat, an open Python toolkit that extracts 255 linguistic features organized into 14 subgroups. Third, it searches widely over transformers, classical learners, and feature sets, then builds hybrids in which transformer predictions become extra inputs to a non-neural classifier. On WeeBit, OneStopEnglish, and Cambridge, the hybrids reach state-of-the-art accuracy. RoBERTa-RF-T1 scores about 99% on OneStopEnglish, 20.3% above the earlier best result. The work matters because it shows that handcrafted linguistic knowledge still pays off when labelled data is scarce, which is the usual situation in RA.

## Key points
- **Topic-based features.** Online LDA models were trained on an English Wikipedia dump (20,210,301), WeeBit, and OneStopEnglish, each with 50, 100, 150, and 200 topics.
  - Every model yields four features: richness, clarity, noise, and the number of discovered topics.
  - These form the WoKF, WBKF, and OSKF subgroups.
- **Headline results.**
  - RoBERTa-RF-T1 reaches about 99% accuracy on OneStopEnglish.
  - On Cambridge, the smallest dataset, hybrids beat the transformer alone by roughly 10% on average.
- **Stronger BERT baseline.** The authors' plain BERT scored 89.3% on WeeBit, versus 83.9% (Deutsch et al., 2020) and 85.7% (Martinc et al., 2021). They credit this mainly to a 512-token input length instead of 128.
- **Data efficiency.** Trained on only 750 class-balanced passages (30% of the WeeBit training set), BERT-GB-T1 achieves 85.8%, edging past the earlier 85.7% SOTA.
- **Data-size trend.** In the learning-curve experiment:
  - the hybrid outperformed BERT at every training size by 0.01–0.05, with the gap narrowing as data grew;
  - BERT consistently outperformed XGBoost.

## Interesting ideas
- **Describe the distribution, not the topics.** Each document's topic probabilities are sorted and topic IDs are discarded. Shape statistics are then computed: a weighted sum for richness, the mean gap from the top probability for clarity, and Fisher kurtosis for noise. This yields compact, topic-agnostic features that could be reused for any corpus or language with a trained topic model.
- **Judge features by what they add to the model.** The authors value a new feature for the extra information it contributes to the whole model, not for its individual correlation with the label. This is a useful criterion whenever new features may overlap with existing ones.
- **Stacking instead of concatenation.** Transformer outputs are treated as features for a classical learner such as Random Forest or XGBoost, rather than appending handcrafted features directly to embeddings. The authors argue this is more robust on small datasets. It is a cheap recipe for low-resource classification tasks.
- **Learning curves on a fixed test set.** Training sets were subsampled in 50-passage steps with balanced classes, and every model was evaluated on the same held-out data. This cleanly shows when handcrafted features stop helping and is a good template for deciding whether feature engineering is worth the effort.
- **Check input length before comparing baselines.** Before trusting a weak transformer baseline, count how many documents exceed the sequence limit. Truncation alone can explain large accuracy gaps between published results.

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

## Institutions

- [University of Pennsylvania](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) — University, Philadelphia, United States · 170 works · 60,739 citations
- [University of Wisconsin–Madison](https://md.research.elamur.ai/free/en/universities/university-of-wisconsin-madison/) — University, Madison, United States · 21 works · 46,892 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

## Cite

Cite as: Bruce W. Lee, Yoo Sung Jang and Jason Lee. 2021. Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{lee2021pushing,
  title = {{Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features}},
  author = {Bruce W. Lee, Yoo Sung Jang and Jason Lee},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  year = {2021},
  doi = {10.18653/v1/2021.emnlp-main.834},
  url = {https://doi.org/10.18653/v1/2021.emnlp-main.834}
}
```

- [← previous End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-neural-retrievers/)
- [next → Aspect-Controllable Opinion Summarization](https://md.research.elamur.ai/free/en/papers/amplayo-2021-aspect-controllable-opinion-summarization/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/)

## References

95 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. [99% accuracy on OneStopEnglish (table 5) shows that our model is capable of almost perfectly capturing the linguistic properties relating to readability on certain datasets. This is a positive and abnormally quick improvement, considering that the reported RA accuracies have never exceeded 90% on po…](https://md.research.elamur.ai/free/en/papers/aluisio-2010-readability-assessment-text-simplification/)
2. David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. [Latent dirichlet allocation](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/). the Journal of machine Learning research, 3:993–1022.
3. OV Blinova, Tarasov NA, Modina VV, and IS Blekanov. 2020. Modeling lemma frequency bands for lexical complexity assessment of russian texts1. In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”, Moscow, June 17–20, 2020, pages 76–92… [find](https://scholar.google.com/scholar?q=OV+Blinova%2C+Tarasov+NA%2C+Modina+VV%2C+and+IS+Blekanov%2E+2020%2E+Modeling+lemma+frequency+bands+for+lexical+complexity+assessment+of+russian+texts1%2E+In+Computational+Linguistics+and+Intellectual+Technologies)
4. Leo Breiman. 2001. Random forests. Machine learning, 45(1):5–32. [find](https://scholar.google.com/scholar?q=Leo+Breiman%2E+2001%2E+Random+forests%2E+Machine+learning%2C+45%281%29%3A5%E2%80%9332%2E)
5. Marc Brysbaert and Boris New. 2009. Moving beyond kučera and francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for american english. Behavior research methods, 41(4):977–990. [find](https://scholar.google.com/scholar?q=Marc+Brysbaert+and+Boris+New%2E+2009%2E+Moving+beyond+kuc%CC%8Cera+and+francis%3A+A+critical+evaluation+of+current+word+frequency+norms+and+the+introduction+of+a+new+and+improved+word+frequency+measure+for+ameri)
6. Lori Buchanan, Chris Westbury, and Curt Burgess. 2001. Characterizing semantic space: Neighborhood effects in word recognition. Psychonomic Bulletin & Review, 8(3):531–544. [find](https://scholar.google.com/scholar?q=Lori+Buchanan%2C+Chris+Westbury%2C+and+Curt+Burgess%2E+2001%2E+Characterizing+semantic+space%3A+Neighborhood+effects+in+word+recognition%2E+Psychonomic+Bulletin+%26+Review%2C+8%283%29%3A531%E2%80%93544%2E)
7. Chih-Chung Chang and Chih-Jen Lin. 2011. Libsvm: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1–27. [find](https://scholar.google.com/scholar?q=Chih%2DChung+Chang+and+Chih%2DJen+Lin%2E+2011%2E+Libsvm%3A+a+library+for+support+vector+machines%2E+ACM+transactions+on+intelligent+systems+and+technology+%28TIST%29%2C+2%283%29%3A1%E2%80%9327%2E)
8. Jing Chen, James H Fife, Isaac I Bejar, and André A Rupp. 2016. Building e-rater® scoring models using machine learning methods. ETS Research Report Series, 2016(1):1–12. [find](https://scholar.google.com/scholar?q=Jing+Chen%2C+James+H+Fife%2C+Isaac+I+Bejar%2C+and+Andr%C3%A9+A+Rupp%2E+2016%2E+Building+e%2Drater%C2%AE+scoring+models+using+machine+learning+methods%2E+ETS+Research+Report+Series%2C+2016%281%29%3A1%E2%80%9312%2E)
9. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
10. Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785– 794. [find](https://scholar.google.com/scholar?q=Tianqi+Chen+and+Carlos+Guestrin%2E+2016%2E+Xgboost%3A+A+scalable+tree+boosting+system%2E+In+Proceedings+of+the+22nd+acm+sigkdd+international+conference+on+knowledge+discovery+and+data+mining%2C+pages+785%E2%80%93+794%2E)
11. Adam EM Eltorai, Syed S Naqvi, Soha Ghanian, Craig P Eberson, Arnold-Peter C Weiss, Christopher T Born, and Alan H Daniels. 2015. Readability of invasive procedure consent forms. Clinical and translational science, 8(6):830–833. [find](https://scholar.google.com/scholar?q=Adam+EM+Eltorai%2C+Syed+S+Naqvi%2C+Soha+Ghanian%2C+Craig+P+Eberson%2C+Arnold%2DPeter+C+Weiss%2C+Christopher+T+Born%2C+and+Alan+H+Daniels%2E+2015%2E+Readability+of+invasive+procedure+consent+forms%2E+Clinical+and+translat)
12. Alina Maria Ciobanu, Liviu P Dinu, and Flaviu Pepelea. 2015. Readability assessment of translated texts. In Proceedings of the International Conference Recent Advances in Natural Language Processing, pages 97–103. [find](https://scholar.google.com/scholar?q=Alina+Maria+Ciobanu%2C+Liviu+P+Dinu%2C+and+Flaviu+Pepelea%2E+2015%2E+Readability+assessment+of+translated+texts%2E+In+Proceedings+of+the+International+Conference+Recent+Advances+in+Natural+Language+Processing%2C+)
13. Kevyn Collins-Thompson. 2014. Computational assessment of text readability: A survey of current and future research. ITL-International Journal of Applied Linguistics, 165(2):97–135. [find](https://scholar.google.com/scholar?q=Kevyn+Collins%2DThompson%2E+2014%2E+Computational+assessment+of+text+readability%3A+A+survey+of+current+and+future+research%2E+ITL%2DInternational+Journal+of+Applied+Linguistics%2C+165%282%29%3A97%E2%80%93135%2E)
14. Kevyn Collins-Thompson and James P Callan. 2004. [A language modeling approach to predicting reading difficulty](https://md.research.elamur.ai/free/en/papers/thompson-2004-language-modeling-approach-predicting-reading/). In Proceedings of the human language technology conference of the North American chapter of the association for computational linguistics: HLT-NAACL 2004, pages 193–200.
15. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. [Natural language processing (almost) from scratch](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/). Journal of Machine Learning Research, 12(76):2493– 2537.
16. Scott A Crossley, Jerry Greenfield, and Danielle S Mc-Namara. 2008. Assessing text readability using cognitively based indices. Tesol Quarterly, 42(3):475– 493. [find](https://scholar.google.com/scholar?q=Scott+A+Crossley%2C+Jerry+Greenfield%2C+and+Danielle+S+Mc%2DNamara%2E+2008%2E+Assessing+text+readability+using+cognitively+based+indices%2E+Tesol+Quarterly%2C+42%283%29%3A475%E2%80%93+493%2E)
17. Patcharanut Daowadung and Yaw-Huei Chen. 2011. Using word segmentation and svm to assess readability of thai text for primary school students. In 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), pages 170–174. IEEE. [find](https://scholar.google.com/scholar?q=Patcharanut+Daowadung+and+Yaw%2DHuei+Chen%2E+2011%2E+Using+word+segmentation+and+svm+to+assess+readability+of+thai+text+for+primary+school+students%2E+In+2011+Eighth+International+Joint+Conference+on+Computer)
18. Aaron Defazio, Francis Bach, and Simon Lacoste-Julien. 2014. Saga: A fast incremental gradient method with support for non-strongly convex composite objectives. arXiv preprint arXiv:1407.0202. — [arXiv:1407.0202](https://arxiv.org/abs/1407.0202) [find](https://scholar.google.com/scholar?q=Aaron+Defazio%2C+Francis+Bach%2C+and+Simon+Lacoste%2DJulien%2E+2014%2E+Saga%3A+A+fast+incremental+gradient+method+with+support+for+non%2Dstrongly+convex+composite+objectives%2E+arXiv+preprint+arXiv%3A1407%2E0202%2E+%E2%80%94+%5BarXi)
19. Tovly Deutsch, Masoud Jasbi, and Stuart Shieber. 2020. [Linguistic features for readability assessment](https://md.research.elamur.ai/free/en/papers/deutsch-2020-linguistic-features-readability-assessment/). In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 1–17, Seattle, WA, USA Online. Association for Computational Linguistics.
20. Vyvyan Evans. 2006. Cognitive linguistics. Edinburgh University Press. [find](https://scholar.google.com/scholar?q=Vyvyan+Evans%2E+2006%2E+Cognitive+linguistics%2E+Edinburgh+University+Press%2E)
21. Johan Falkenjack, Katarina Heimann Mühlenbock, and Arne Jönsson. 2013. Features indicating readability in swedish text. In Proceedings of the 19th Nordic Conference of Computational Linguistics (NODAL-IDA 2013), pages 27–40. [find](https://scholar.google.com/scholar?q=Johan+Falkenjack%2C+Katarina+Heimann+M%C3%BChlenbock%2C+and+Arne+J%C3%B6nsson%2E+2013%2E+Features+indicating+readability+in+swedish+text%2E+In+Proceedings+of+the+19th+Nordic+Conference+of+Computational+Linguistics+%28NODAL)
22. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. Liblinear: A library for large linear classification. the Journal of machine Learning research, 9:1871–1874. [find](https://scholar.google.com/scholar?q=Rong%2DEn+Fan%2C+Kai%2DWei+Chang%2C+Cho%2DJui+Hsieh%2C+Xiang%2DRui+Wang%2C+and+Chih%2DJen+Lin%2E+2008%2E+Liblinear%3A+A+library+for+large+linear+classification%2E+the+Journal+of+machine+Learning+research%2C+9%3A1871%E2%80%931874%2E)
23. Lijun Feng, Noémie Elhadad, and Matt Huenerfauth. 2009. Cognitively motivated features for readability assessment. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pages 229–237. [find](https://scholar.google.com/scholar?q=Lijun+Feng%2C+No%C3%A9mie+Elhadad%2C+and+Matt+Huenerfauth%2E+2009%2E+Cognitively+motivated+features+for+readability+assessment%2E+In+Proceedings+of+the+12th+Conference+of+the+European+Chapter+of+the+ACL+%28EACL+2009%29%2C)
24. Lijun Feng, Martin Jansche, Matt Huenerfauth, and Noémie Elhadad. 2010. [A comparison of features for automatic readability assessment](https://md.research.elamur.ai/free/en/papers/feng-2010-comparison-features-automatic-readability-assessment/).
25. Anna Filighera, Tim Steuer, and Christoph Rensing. 2019. Automatic text difficulty estimation using embeddings and neural networks. In European Conference on Technology Enhanced Learning, pages 335–348. Springer. [find](https://scholar.google.com/scholar?q=Anna+Filighera%2C+Tim+Steuer%2C+and+Christoph+Rensing%2E+2019%2E+Automatic+text+difficulty+estimation+using+embeddings+and+neural+networks%2E+In+European+Conference+on+Technology+Enhanced+Learning%2C+pages+335%E2%80%9334)
26. Paul R Fitzsimmons, BD Michael, Joane L Hulley, and G Orville Scott. 2010. A readability assessment of online parkinson’s disease information. The journal of the Royal College of Physicians of Edinburgh, 40(4):292–296. [find](https://scholar.google.com/scholar?q=Paul+R+Fitzsimmons%2C+BD+Michael%2C+Joane+L+Hulley%2C+and+G+Orville+Scott%2E+2010%2E+A+readability+assessment+of+online+parkinson%E2%80%99s+disease+information%2E+The+journal+of+the+Royal+College+of+Physicians+of+Edinbur)
27. Thomas François. 2014. An analysis of a french as a foreign language corpus for readability assessment. In Proceedings of the third workshop on NLP for computer-assisted language learning, pages 13–32. [find](https://scholar.google.com/scholar?q=Thomas+Fran%C3%A7ois%2E+2014%2E+An+analysis+of+a+french+as+a+foreign+language+corpus+for+readability+assessment%2E+In+Proceedings+of+the+third+workshop+on+NLP+for+computer%2Dassisted+language+learning%2C+pages+13%E2%80%9332)
28. Benoît Frénay, Ata Kabán, et al. 2014. A comprehensive introduction to label noise. In ESANN. Citeseer. [find](https://scholar.google.com/scholar?q=Beno%C3%AEt+Fr%C3%A9nay%2C+Ata+Kab%C3%A1n%2C+et+al%2E+2014%2E+A+comprehensive+introduction+to+label+noise%2E+In+ESANN%2E+Citeseer%2E)
29. Edward Gibson. 1998. Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1):1–76. [find](https://scholar.google.com/scholar?q=Edward+Gibson%2E+1998%2E+Linguistic+complexity%3A+Locality+of+syntactic+dependencies%2E+Cognition%2C+68%281%29%3A1%E2%80%9376%2E)
30. Camille Guinaudeau and Michael Strube. 2013. Graph-based local coherence modeling. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 93–103. [find](https://scholar.google.com/scholar?q=Camille+Guinaudeau+and+Michael+Strube%2E+2013%2E+Graph%2Dbased+local+coherence+modeling%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+p)
31. John Hale. 2016. Information-theoretical complexity metrics. Language and Linguistics Compass, 10(9):397–412. [find](https://scholar.google.com/scholar?q=John+Hale%2E+2016%2E+Information%2Dtheoretical+complexity+metrics%2E+Language+and+Linguistics+Compass%2C+10%289%29%3A397%E2%80%93412%2E)
32. Hieronymus Hansen, Adam Widera, Johannes Ponge, and Bernd Hellingrath. 2021. Machine learning for readability assessment and text simplification in crisis communication: A systematic review. In Proceedings of the 54th Hawaii International Conference on System Sciences, page 2265. [find](https://scholar.google.com/scholar?q=Hieronymus+Hansen%2C+Adam+Widera%2C+Johannes+Ponge%2C+and+Bernd+Hellingrath%2E+2021%2E+Machine+learning+for+readability+assessment+and+text+simplification+in+crisis+communication%3A+A+systematic+review%2E+In+Procee)
33. Bruce W. Lee and Jason Lee. 2020a. LXPER index 2.0: Improving text readability assessment model for L2 English students in Korea. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 20– 24, Suzhou, China. Association for Computational Ling… [find](https://scholar.google.com/scholar?q=Bruce+W%2E+Lee+and+Jason+Lee%2E+2020a%2E+LXPER+index+2%2E0%3A+Improving+text+readability+assessment+model+for+L2+English+students+in+Korea%2E+In+Proceedings+of+the+6th+Workshop+on+Natural+Language+Processing+Tech)
34. Bruce W Lee and Jason Hyung-Jong Lee. 2020b. Lxper index: a curriculum-specific text readability assessment model for efl students in korea. arXiv preprint arXiv:2008.01564. — [arXiv:2008.01564](https://arxiv.org/abs/2008.01564) [find](https://scholar.google.com/scholar?q=Bruce+W+Lee+and+Jason+Hyung%2DJong+Lee%2E+2020b%2E+Lxper+index%3A+a+curriculum%2Dspecific+text+readability+assessment+model+for+efl+students+in+korea%2E+arXiv+preprint+arXiv%3A2008%2E01564%2E+%E2%80%94+%5BarXiv%3A2008%2E01564%5D%28https)
35. Marti A. Hearst. 1998. Support vector machines. IEEE Intelligent Systems, 13(4):18–28. [find](https://scholar.google.com/scholar?q=Marti+A%2E+Hearst%2E+1998%2E+Support+vector+machines%2E+IEEE+Intelligent+Systems%2C+13%284%29%3A18%E2%80%9328%2E)
36. Gondy Leroy and David Kauchak. 2013. The effect of word familiarity on actual and perceived text difficulty. Journal of the American Medical Informatics Association, 21(e1):e169–e172. [find](https://scholar.google.com/scholar?q=Gondy+Leroy+and+David+Kauchak%2E+2013%2E+The+effect+of+word+familiarity+on+actual+and+perceived+text+difficulty%2E+Journal+of+the+American+Medical+Informatics+Association%2C+21%28e1%29%3Ae169%E2%80%93e172%2E)
37. Michael Heilman, Kevyn Collins-Thompson, Jamie Callan, and Maxine Eskenazi. 2007. Combining lexical and grammatical features to improve readability measures for first and second language texts. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for C… [find](https://scholar.google.com/scholar?q=Michael+Heilman%2C+Kevyn+Collins%2DThompson%2C+Jamie+Callan%2C+and+Maxine+Eskenazi%2E+2007%2E+Combining+lexical+and+grammatical+features+to+improve+readability+measures+for+first+and+second+language+texts%2E+In+Hum)
38. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. [BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proceedings of the 58th Annual Meeting of the…
39. Matthew Hoffman, Francis R Bach, and David M Blei. 2010. Online learning for latent dirichlet allocation. In advances in neural information processing systems, pages 856–864. Citeseer. [find](https://scholar.google.com/scholar?q=Matthew+Hoffman%2C+Francis+R+Bach%2C+and+David+M+Blei%2E+2010%2E+Online+learning+for+latent+dirichlet+allocation%2E+In+advances+in+neural+information+processing+systems%2C+pages+856%E2%80%93864%2E+Citeseer%2E)
40. Ping Li, Curt Burgess, and Kevin Lund. 2000. The acquisition of word meaning through global lexical co-occurrences. In Proceedings of the thirtieth annual child language research forum, pages 166–178. Citeseer. [find](https://scholar.google.com/scholar?q=Ping+Li%2C+Curt+Burgess%2C+and+Kevin+Lund%2E+2000%2E+The+acquisition+of+word+meaning+through+global+lexical+co%2Doccurrences%2E+In+Proceedings+of+the+thirtieth+annual+child+language+research+forum%2C+pages+166%E2%80%93178%2E)
41. Thomas Holtgraves. 1999. Comprehending indirect replies: When and how are their conveyed meanings activated? Journal of Memory and Language, 41(4):519–540. [find](https://scholar.google.com/scholar?q=Thomas+Holtgraves%2E+1999%2E+Comprehending+indirect+replies%3A+When+and+how+are+their+conveyed+meanings+activated%3F+Journal+of+Memory+and+Language%2C+41%284%29%3A519%E2%80%93540%2E)
42. Matthew Honnibal, Ines Montani, Sofie Van Lan-deghem, and Adriane Boyd. 2020. spaCy: Industrial-strength Natural Language Processing in Python. [find](https://scholar.google.com/scholar?q=Matthew+Honnibal%2C+Ines+Montani%2C+Sofie+Van+Lan%2Ddeghem%2C+and+Adriane+Boyd%2E+2020%2E+spaCy%3A+Industrial%2Dstrength+Natural+Language+Processing+in+Python%2E)
43. J. Kincaid, R. P. Fishburne, R. L. Rogers, and B. S. Chissom. 1975. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. [find](https://scholar.google.com/scholar?q=J%2E+Kincaid%2C+R%2E+P%2E+Fishburne%2C+R%2E+L%2E+Rogers%2C+and+B%2E+S%2E+Chissom%2E+1975%2E+Derivation+of+new+readability+formulas+%28automated+readability+index%2C+fog+count+and+flesch+reading+ease+formula%29+for+navy+enlisted+pe)
44. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [find](https://scholar.google.com/scholar?q=Diederik+P+Kingma+and+Jimmy+Ba%2E+2014%2E+Adam%3A+A+method+for+stochastic+optimization%2E+arXiv+preprint+arXiv%3A1412%2E6980%2E)
45. Stephen Kokoska and Daniel Zwillinger. 2000. CRC standard probability and statistics tables and formulae. Crc Press. [find](https://scholar.google.com/scholar?q=Stephen+Kokoska+and+Daniel+Zwillinger%2E+2000%2E+CRC+standard+probability+and+statistics+tables+and+formulae%2E+Crc+Press%2E)
46. Victor Kuperman, Hans Stadthagen-Gonzalez, and Marc Brysbaert. 2012. Age-of-acquisition ratings for 30,000 english words. Behavior research methods, 44(4):978–990. [find](https://scholar.google.com/scholar?q=Victor+Kuperman%2C+Hans+Stadthagen%2DGonzalez%2C+and+Marc+Brysbaert%2E+2012%2E+Age%2Dof%2Dacquisition+ratings+for+30%2C000+english+words%2E+Behavior+research+methods%2C+44%284%29%3A978%E2%80%93990%2E)
47. Thomas K Landauer, Kirill Kireyev, and Charles Panaccione. 2011. Word maturity: A new metric for word knowledge. Scientific Studies of Reading, 15(1):92– 108. [find](https://scholar.google.com/scholar?q=Thomas+K+Landauer%2C+Kirill+Kireyev%2C+and+Charles+Panaccione%2E+2011%2E+Word+maturity%3A+A+new+metric+for+word+knowledge%2E+Scientific+Studies+of+Reading%2C+15%281%29%3A92%E2%80%93+108%2E)
48. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. [find](https://scholar.google.com/scholar?q=Yinhan+Liu%2C+Myle+Ott%2C+Naman+Goyal%2C+Jingfei+Du%2C+Mandar+Joshi%2C+Danqi+Chen%2C+Omer+Levy%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Veselin+Stoyanov%2E+2019%2E+Roberta%3A+A+robustly+optimized+bert+pretraining+approach%2E+a)
49. Edward Loper and Steven Bird. 2002. Nltk: The natural language toolkit. arXiv preprint cs/0205028. [find](https://scholar.google.com/scholar?q=Edward+Loper+and+Steven+Bird%2E+2002%2E+Nltk%3A+The+natural+language+toolkit%2E+arXiv+preprint+cs%2F0205028%2E)
50. Xiaofei Lu. 2010. Automatic analysis of syntactic complexity in second language writing. International journal of corpus linguistics, 15(4):474–496. [find](https://scholar.google.com/scholar?q=Xiaofei+Lu%2E+2010%2E+Automatic+analysis+of+syntactic+complexity+in+second+language+writing%2E+International+journal+of+corpus+linguistics%2C+15%284%29%3A474%E2%80%93496%2E)
51. Xiaofei Lu. 2011. A corpus-based evaluation of syntactic complexity measures as indices of college-level esl writers’ language development. TESOL quarterly, 45(1):36–62. [find](https://scholar.google.com/scholar?q=Xiaofei+Lu%2E+2011%2E+A+corpus%2Dbased+evaluation+of+syntactic+complexity+measures+as+indices+of+college%2Dlevel+esl+writers%E2%80%99+language+development%2E+TESOL+quarterly%2C+45%281%29%3A36%E2%80%9362%2E)
52. Yi Ma, Eric Fosler-Lussier, and Robert Lofthus. 2012. Ranking-based readability assessment for early primary children’s literature. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 548–552. [find](https://scholar.google.com/scholar?q=Yi+Ma%2C+Eric+Fosler%2DLussier%2C+and+Robert+Lofthus%2E+2012%2E+Ranking%2Dbased+readability+assessment+for+early+primary+children%E2%80%99s+literature%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+)
53. David Malvern and Brian Richards. 2012. Measures of lexical richness. The encyclopedia of applied linguistics. [find](https://scholar.google.com/scholar?q=David+Malvern+and+Brian+Richards%2E+2012%2E+Measures+of+lexical+richness%2E+The+encyclopedia+of+applied+linguistics%2E)
54. Matej Martinc, Senja Pollak, and Marko Robnik-Šikonja. 2021. [Supervised and Unsupervised Neural Approaches to Text Readability](https://md.research.elamur.ai/free/en/papers/martinc-2021-supervised-unsupervised-neural-approaches-text/). Computational Linguistics, pages 1–39.
55. G Harry Mc Laughlin. 1969. Smog grading-a new readability formula. Journal of reading, 12(8):639–646. [find](https://scholar.google.com/scholar?q=G+Harry+Mc+Laughlin%2E+1969%2E+Smog+grading%2Da+new+readability+formula%2E+Journal+of+reading%2C+12%288%29%3A639%E2%80%93646%2E)
56. Philip M McCarthy and Scott Jarvis. 2010. Mtld, vocd-d, and hd-d: A validation study of sophisticated approaches to lexical diversity assessment. Behavior research methods, 42(2):381–392. [find](https://scholar.google.com/scholar?q=Philip+M+McCarthy+and+Scott+Jarvis%2E+2010%2E+Mtld%2C+vocd%2Dd%2C+and+hd%2Dd%3A+A+validation+study+of+sophisticated+approaches+to+lexical+diversity+assessment%2E+Behavior+research+methods%2C+42%282%29%3A381%E2%80%93392%2E)
57. Danielle S McNamara, Max M Louwerse, Philip M McCarthy, and Arthur C Graesser. 2010. Coh-metrix: Capturing linguistic features of cohesion. Discourse Processes, 47(4):292–330. [find](https://scholar.google.com/scholar?q=Danielle+S+McNamara%2C+Max+M+Louwerse%2C+Philip+M+McCarthy%2C+and+Arthur+C+Graesser%2E+2010%2E+Coh%2Dmetrix%3A+Capturing+linguistic+features+of+cohesion%2E+Discourse+Processes%2C+47%284%29%3A292%E2%80%93330%2E)
58. Changping Meng, Muhao Chen, Jie Mao, and Jennifer Neville. 2020. Readnet: A hierarchical transformer framework for web article readability analysis. In Advances in Information Retrieval, pages 33–49, Cham. Springer International Publishing. [find](https://scholar.google.com/scholar?q=Changping+Meng%2C+Muhao+Chen%2C+Jie+Mao%2C+and+Jennifer+Neville%2E+2020%2E+Readnet%3A+A+hierarchical+transformer+framework+for+web+article+readability+analysis%2E+In+Advances+in+Information+Retrieval%2C+pages+33%E2%80%9349%2C+)
59. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. [Efficient estimation of word representations in vector space](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/). arXiv preprint arXiv:1301.3781.
60. Hamid Mohammadi and Seyed Hossein Khasteh. 2020. A machine learning approach to persian text readability assessment using a crowdsourced dataset. In 2020 28th Iranian Conference on Electrical Engineering (ICEE), pages 1–7. IEEE. [find](https://scholar.google.com/scholar?q=Hamid+Mohammadi+and+Seyed+Hossein+Khasteh%2E+2020%2E+A+machine+learning+approach+to+persian+text+readability+assessment+using+a+crowdsourced+dataset%2E+In+2020+28th+Iranian+Conference+on+Electrical+Engineer)
61. SungJin Nam, Gwen Frishkoff, and Kevyn Collins-Thompson. 2017. Predicting short-and long-term vocabulary learning via semantic features of partial word knowledge. International Educational Data Mining Society. [find](https://scholar.google.com/scholar?q=SungJin+Nam%2C+Gwen+Frishkoff%2C+and+Kevyn+Collins%2DThompson%2E+2017%2E+Predicting+short%2Dand+long%2Dterm+vocabulary+learning+via+semantic+features+of+partial+word+knowledge%2E+International+Educational+Data+Mining)
62. Diego Palma and John Atkinson. 2018. Coherence-based automatic essay assessment. IEEE Intelligent Systems, 33(5):26–36. [find](https://scholar.google.com/scholar?q=Diego+Palma+and+John+Atkinson%2E+2018%2E+Coherence%2Dbased+automatic+essay+assessment%2E+IEEE+Intelligent+Systems%2C+33%285%29%3A26%E2%80%9336%2E)
63. Diego Palma, Christian Soto, Mónica Veliz, Bernardo Riffo, and Antonio Gutiérrez. 2019. A data-driven methodology to assess text complexity based on syntactic and semantic measurements. In International Conference on Human Interaction and Emerging Technologies, pages 509–515. Springer. [find](https://scholar.google.com/scholar?q=Diego+Palma%2C+Christian+Soto%2C+M%C3%B3nica+Veliz%2C+Bernardo+Riffo%2C+and+Antonio+Guti%C3%A9rrez%2E+2019%2E+A+data%2Ddriven+methodology+to+assess+text+complexity+based+on+syntactic+and+semantic+measurements%2E+In+Internation)
64. Mary Anne Peabody and Charles E Schaefer. 2016. Towards semantic clarity in play therapy. International Journal of Play Therapy, 25(4):197. [find](https://scholar.google.com/scholar?q=Mary+Anne+Peabody+and+Charles+E+Schaefer%2E+2016%2E+Towards+semantic+clarity+in+play+therapy%2E+International+Journal+of+Play+Therapy%2C+25%284%29%3A197%2E)
65. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830. [find](https://scholar.google.com/scholar?q=Fabian+Pedregosa%2C+Ga%C3%ABl+Varoquaux%2C+Alexandre+Gramfort%2C+Vincent+Michel%2C+Bertrand+Thirion%2C+Olivier+Grisel%2C+Mathieu+Blondel%2C+Peter+Prettenhofer%2C+Ron+Weiss%2C+Vincent+Dubourg%2C+et+al%2E+2011%2E+Scikit%2Dlearn%3A+Mach)
66. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. [GloVe: Global vectors for word representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.
67. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. [Deep contextualized word representations](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolog…
68. S. E. Petersen and Mari Ostendorf. 2009. A machine learning approach to reading level assessment. Comput. Speech Lang., 23:89–106. [find](https://scholar.google.com/scholar?q=S%2E+E%2E+Petersen+and+Mari+Ostendorf%2E+2009%2E+A+machine+learning+approach+to+reading+level+assessment%2E+Comput%2E+Speech+Lang%2E%2C+23%3A89%E2%80%93106%2E)
69. Penny M Pexman, Ian S Hargreaves, Paul D Siakaluk, Glen E Bodner, and Jamie Pope. 2008. There are many ways to be rich: Effects of three measures of semantic richness on visual word recognition. Psychonomic Bulletin & Review, 15(1):161–167. [find](https://scholar.google.com/scholar?q=Penny+M+Pexman%2C+Ian+S+Hargreaves%2C+Paul+D+Siakaluk%2C+Glen+E+Bodner%2C+and+Jamie+Pope%2E+2008%2E+There+are+many+ways+to+be+rich%3A+Effects+of+three+measures+of+semantic+richness+on+visual+word+recognition%2E+Psych)
70. Carla Pires, Afonso Cavaco, and Marina Vigário. 2017. Towards the definition of linguistic metrics for evaluating text readability. Journal of Quantitative Linguistics, 24(4):319–349. [find](https://scholar.google.com/scholar?q=Carla+Pires%2C+Afonso+Cavaco%2C+and+Marina+Vig%C3%A1rio%2E+2017%2E+Towards+the+definition+of+linguistic+metrics+for+evaluating+text+readability%2E+Journal+of+Quantitative+Linguistics%2C+24%284%29%3A319%E2%80%93349%2E)
71. Emily Pitler and Ani Nenkova. 2008. [Revisiting readability: A unified framework for predicting text quality](https://md.research.elamur.ai/free/en/papers/pitler-2008-revisiting-readability-unified-framework-predicting/). In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 186–195, Honolulu, Hawaii. Association for Computational Linguistics.
72. John C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61–74. MIT Press. [find](https://scholar.google.com/scholar?q=John+C%2E+Platt%2E+1999%2E+Probabilistic+outputs+for+support+vector+machines+and+comparisons+to+regularized+likelihood+methods%2E+In+ADVANCES+IN+LARGE+MARGIN+CLASSIFIERS%2C+pages+61%E2%80%9374%2E+MIT+Press%2E)
73. Rani Qumsiyeh and Yiu-Kai Ng. 2011. Readaid: a robust and fully-automated readability assessment tool. In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pages 539–546. IEEE. [find](https://scholar.google.com/scholar?q=Rani+Qumsiyeh+and+Yiu%2DKai+Ng%2E+2011%2E+Readaid%3A+a+robust+and+fully%2Dautomated+readability+assessment+tool%2E+In+2011+IEEE+23rd+International+Conference+on+Tools+with+Artificial+Intelligence%2C+pages+539%E2%80%93546%2E+)
74. Radim Řehůřek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45–50, Valletta, Malta. ELRA. [find](https://scholar.google.com/scholar?q=Radim+R%CC%8Cehu%CC%8Ar%CC%8Cek+and+Petr+Sojka%2E+2010%2E+Software+Framework+for+Topic+Modelling+with+Large+Corpora%2E+In+Proceedings+of+the+LREC+2010+Workshop+on+New+Challenges+for+NLP+Frameworks%2C+pages+45%E2%80%9350%2C+Valletta%2C+)
75. Mark Schmidt, Nicolas Le Roux, and Francis Bach. 2017. Minimizing finite sums with the stochastic average gradient. Mathematical Programming, 162(1-2):83–112. [find](https://scholar.google.com/scholar?q=Mark+Schmidt%2C+Nicolas+Le+Roux%2C+and+Francis+Bach%2E+2017%2E+Minimizing+finite+sums+with+the+stochastic+average+gradient%2E+Mathematical+Programming%2C+162%281%2D2%29%3A83%E2%80%93112%2E)
76. Sarah E. Schwarm and Mari Ostendorf. 2005. [Reading level assessment using support vector machines and statistical language models](https://md.research.elamur.ai/free/en/papers/schwarm-2005-reading-level-assessment-support-vector/). In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL ’05, page 523–530, USA. Association for Computational Linguistics.
77. Luo Si and Jamie Callan. 2001. A statistical model for scientific readability. In Proceedings of the tenth international conference on Information and knowledge management, pages 574–576. [find](https://scholar.google.com/scholar?q=Luo+Si+and+Jamie+Callan%2E+2001%2E+A+statistical+model+for+scientific+readability%2E+In+Proceedings+of+the+tenth+international+conference+on+Information+and+knowledge+management%2C+pages+574%E2%80%93576%2E)
78. Sanja Štajner and Sergiu Nisioi. 2018. A detailed evaluation of neural sequence-to-sequence models for in-domain and cross-domain text simplification. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018). [find](https://scholar.google.com/scholar?q=Sanja+%C5%A0tajner+and+Sergiu+Nisioi%2E+2018%2E+A+detailed+evaluation+of+neural+sequence%2Dto%2Dsequence+models+for+in%2Ddomain+and+cross%2Ddomain+text+simplification%2E+In+Proceedings+of+the+eleventh+international+conf)
79. Wei Xu, Chris Callison-Burch, and Courtney Napoles. 2015. [Problems in current text simplification research: New data can help](https://md.research.elamur.ai/free/en/papers/xu-2015-problems-current-text-simplification-research/). Transactions of the Association for Computational Linguistics, 3:283–297.
80. Kumiko Tanaka-Ishii, Satoshi Tezuka, and Hiroshi Terada. 2010. Sorting texts by readability. Computational Linguistics, 36(2):203–227. [find](https://scholar.google.com/scholar?q=Kumiko+Tanaka%2DIshii%2C+Satoshi+Tezuka%2C+and+Hiroshi+Terada%2E+2010%2E+Sorting+texts+by+readability%2E+Computational+Linguistics%2C+36%282%29%3A203%E2%80%93227%2E)
81. Victoria Yaneva, Constantin Orăsan, Richard Evans, and Omid Rohanian. 2017. Combining multiple corpora for readability assessment for people with cognitive disabilities. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Victoria+Yaneva%2C+Constantin+Ora%CC%86san%2C+Richard+Evans%2C+and+Omid+Rohanian%2E+2017%2E+Combining+multiple+corpora+for+readability+assessment+for+people+with+cognitive+disabilities%2E+Association+for+Computational)
82. Sara Tonelli, Ke M Tran, and Emanuele Pianta. 2012. Making readability indices readable. In Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, pages 40–48. [find](https://scholar.google.com/scholar?q=Sara+Tonelli%2C+Ke+M+Tran%2C+and+Emanuele+Pianta%2E+2012%2E+Making+readability+indices+readable%2E+In+Proceedings+of+the+First+Workshop+on+Predicting+and+Improving+Text+Readability+for+target+reader+populations)
83. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. [Xlnet: Generalized autoregressive pretraining for language understanding](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/). Advances in Neural Information Processing Systems, 32:5753–5763.
84. Sowmya Vajjala. 2021. Trends, limitations and open challenges in automatic readability assessment research. arXiv preprint arXiv:2105.00973. [find](https://scholar.google.com/scholar?q=Sowmya+Vajjala%2E+2021%2E+Trends%2C+limitations+and+open+challenges+in+automatic+readability+assessment+research%2E+arXiv+preprint+arXiv%3A2105%2E00973%2E)
85. Shuyu Zhang, Xuanyu Zhou, Huifeng Pan, and Junyi Jia. 2019. Cryptocurrency, confirmatory bias and news readability–evidence from the largest chinese cryptocurrency exchange. Accounting & Finance, 58(5):1445–1468. [find](https://scholar.google.com/scholar?q=Shuyu+Zhang%2C+Xuanyu+Zhou%2C+Huifeng+Pan%2C+and+Junyi+Jia%2E+2019%2E+Cryptocurrency%2C+confirmatory+bias+and+news+readability%E2%80%93evidence+from+the+largest+chinese+cryptocurrency+exchange%2E+Accounting+%26+Finance%2C+58%285)
86. Sowmya Vajjala and Ivana Lučić. 2018. [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/). In Proceedings of the thirteenth workshop on innovative use of NLP for building educational applications, pages 297–304.
87. Sowmya Vajjala and Detmar Meurers. 2012. [On improving the accuracy of readability classification using insights from second language acquisition](https://md.research.elamur.ai/free/en/papers/vajjala-2012-improving-accuracy-readability-classification-insights/). In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 163–173, Montréal, Canada. Association for Computational Ling…
88. Sowmya Vajjala and Detmar Meurers. 2013. On the applicability of readability models to web texts. In Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations, pages 59–68. [find](https://scholar.google.com/scholar?q=Sowmya+Vajjala+and+Detmar+Meurers%2E+2013%2E+On+the+applicability+of+readability+models+to+web+texts%2E+In+Proceedings+of+the+Second+Workshop+on+Predicting+and+Improving+Text+Readability+for+Target+Reader+P)
89. Sowmya Vajjala and Detmar Meurers. 2016. Readability-based sentence ranking for evaluating text simplification. arXiv preprint arXiv:1603.06009. [find](https://scholar.google.com/scholar?q=Sowmya+Vajjala+and+Detmar+Meurers%2E+2016%2E+Readability%2Dbased+sentence+ranking+for+evaluating+text+simplification%2E+arXiv+preprint+arXiv%3A1603%2E06009%2E)
90. [Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lho…](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
91. Menglin Xia, Ekaterina Kochmar, and Ted Briscoe. 2016. [Text readability assessment for second language learners](https://md.research.elamur.ai/free/en/papers/xia-2016-text-readability-assessment-second-language/). In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 12–22, San Diego, CA. Association for Computational Linguistics.
92. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. [Character-level convolutional networks for text classification](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/). In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc.
93. Yu Zhang, Houquan Zhou, and Zhenghua Li. 2020. Fast and accurate neural CRF constituency parsing. In Proceedings of IJCAI, pages 4046–4053. [find](https://scholar.google.com/scholar?q=Yu+Zhang%2C+Houquan+Zhou%2C+and+Zhenghua+Li%2E+2020%2E+Fast+and+accurate+neural+CRF+constituency+parsing%2E+In+Proceedings+of+IJCAI%2C+pages+4046%E2%80%934053%2E)
94. Ciyou Zhu, Richard Byrd, Jorge Nocedal, and Jose Luis Morales. 2011. L-bfgs-b. Retrieved Feb, 18:2012. [find](https://scholar.google.com/scholar?q=Ciyou+Zhu%2C+Richard+Byrd%2C+Jorge+Nocedal%2C+and+Jose+Luis+Morales%2E+2011%2E+L%2Dbfgs%2Db%2E+Retrieved+Feb%2C+18%3A2012%2E)
95. Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2009. A multi-dimensional model for assessing the quality of answers in social Q&A sites. Ph.D. thesis. [find](https://scholar.google.com/scholar?q=Zhemin+Zhu%2C+Delphine+Bernhard%2C+and+Iryna+Gurevych%2E+2009%2E+A+multi%2Ddimensional+model+for+assessing+the+quality+of+answers+in+social+Q%26A+sites%2E+Ph%2ED%2E+thesis%2E)

*Updated 24 October 2026*