Cover not available

Article published In: Recent Advances in Automatic Readability Assessment and Text Simplification
Edited by Thomas François and Delphine Bernhard
[ITL - International Journal of Applied Linguistics 165:2] 2014
► pp. 97–135

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
authorization required

Computational assessment of text readability

A survey of current and future research

 | University of Michigan
Published online: 23 January 2015
DOI logo with link
https://doi.org/10.1075/itl.165.2.01col
Assessing text readability is a time-honored problem that has even more relevance in today’s information-rich world. This article provides background on how readability of texts is assessed automatically, reviews the current state-of-the-art algorithms in automatic modeling and predicting the reading difficulty of texts, and proposes new challenges and opportunities for future exploration not well-covered by current computational research.
References (97)
Abedi, J., Leon, S., Kao, J., Bayley, R., Ewers, N., Herman, J., & Mundhenk, K. (2011). Accessible reading assessments for students with disabilities: The role of cognitive, grammatical, lexical, and textual/visual features. CRESST Report #785. Univ. of California, Los Angeles. Jan 2011. [URL]
Agrawal, R., Gollapudi, S., Kannan, A., & Kenthapadi, K. (2011). Identifying enrichment candidates in textbooks. Proceedings of the 20th International Conference on World Wide Web (WWW ’11) (pp. 483–492). ACM, New York, NY, USA.
Akamatsu, K., Pattanasri, N., Jatowt, A., & Tanaka, K. (2011). Measuring comprehensibility of web pages based on link analysis. Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 11, pp. 40–46).
Al-Khalifa, H.S., & Al-Ajlan, A.A. (2010). Automatic readability measurements of the Arabic text: An exploratory study. Arabian Journal for Science and Engineering, 35(2c) (pp. 103–124).Google Scholar logo with link to Google Scholar
Aluisio, S., Specia, L., Gasperin, C. and Scarton, C. 2010. Readability Assessment for Text Simplification. In Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications, (pp. 1–9).
Barzilay, R., & Elhadad, N., (2003). Sentence alignment for monolingual comparable Corpora. Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP’03) (pp. 25–32).
Bates, E. (2003). On the nature and nurture of language. In R. Levi-Montalcini, D. Baltimore, R. Dulbecco & F. Jacob (Series Eds.) & E. Bizzi, P. Calissano & V. Volterra (Vol. Eds.), Frontiers of biology: The brain of homo sapiens (pp. 241–265). Rome: Istituto della Enciclopedia Italiana fondata da Giovanni Trecanni S.p. A.Google Scholar logo with link to Google Scholar
Becker, S.A. (2004). A study of web usability for older adults seeking online health resources. ACM Transactions on Computer-Human Interaction (TOCHI), 11(4), 387–406. Google Scholar logo with link to Google Scholar
Beinborn, L., Zesch, T., & Gurevych, I. (2012). Towards fine-grained readability measures for self-directed language learning. Proceedings of the SLTC 2012 Workshop on NLP for CALL: Linkoping Electronic Conference, 801, 11–19.Google Scholar logo with link to Google Scholar
Benjamin, R. (2012). Reconstructing readability: Recent developments and recommendations in the analysis of text difficulty. Educational Psychology Review, 24(1), 63–88. Google Scholar logo with link to Google Scholar
Carroll, J.B., Davies, P., & Richman, B. (1971). Word frequency book. Boston: Houghton Mifflin.Google Scholar logo with link to Google Scholar
Chall, J.S. (1958). Readability: An appraisal of research and application. Bureau of Educational Research Monographs, No. 34. Columbus: Ohio State University Press.Google Scholar logo with link to Google Scholar
Chall, J.S., & Dale, E. (1995). Readability revisited: The New Dale-Chall readability formula. Cambridge, MA: Brookline Books.Google Scholar logo with link to Google Scholar
Chang, K.M., Nelson, J., Pant, U., & Mostow, J. (2013). Toward exploiting EEG input in a reading tutor. International Journal of Artificial Intelligence in Education, 22(1-2), 19–38.Google Scholar logo with link to Google Scholar
Chen, X., Bennett, P.N., Collins-Thompson, K., & Horvitz, E. (2013). Pairwise ranking aggregation in a crowdsourced setting. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM ’13) (pp. 193–202). ACM, New York, NY, USA.
Chen, Y.-T., Chen, Y.-H., & Cheng, Y.-C. (2013). Assessing Chinese readability using term frequency and lexical chains. Computational Linguistics and Chinese Language Processing, 181, 1–18.Google Scholar logo with link to Google Scholar
Cole, M.J., Gwizdka, J., Liu, C., Belkin, N.J., & Zhang, X. (2012). Inferring user knowledge level from eye movement patterns. Information Processing and Management. Google Scholar logo with link to Google Scholar
Collins-Thompson, K., Bennett, P.N., White, R.W., de la Chica, S., & Sontag, D. (2011). Personalizing web search results by reading level. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM ’11) (pp. 403–412). ACM, New York, NY, USA.
Collins-Thompson, K., & Callan, J. (2004b). Information retrieval for language tutoring: An overview of the REAP project. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’04) (pp. 544–545). ACM, New York, NY, USA.
Collins-Thompson, K, & Callan, J. (2004c). A language modeling approach to predicting reading difficulty. Proceedings of HLT-NAACL 2004 (pp. 193–200).
Collins-Thompson, K., & Callan, J. (2005). Predicting reading difficulty with statistical language models. Journal of the American Society for Information Science and Technology, 561, 1448–1462. Google Scholar logo with link to Google Scholar
Collins-Thompson, K. (2013). Enriching the web by modeling reading difficulty. Proceedings of the Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR ‘13) (pp. 3–4). ACM, New York, NY, USA.
Crossley, S.A., Greenfield, J., & McNamara, D.S. (2008). Assessing text readability using cognitively based indices. TESOL Quarterly, 42(3), 475–493. Google Scholar logo with link to Google Scholar
Dale, E., & Chall, J.S. (1949). The concept of readability. Consciousness and Cognition, 26(23).Google Scholar logo with link to Google Scholar
Dale, E., & O’Rourke, J. (1981). The living word vocabulary. Chicago, IL: World Book/Childcraft International.Google Scholar logo with link to Google Scholar
Daowadung, P., & Chen, Y.-H. (2011). Using word segmentation and SVM to assess readability of Thai text for primary school students. Proceedings of the International Joint Conference on Computer Science and Software Engineering: JCSSE .
Dascalu, M. (2014). ReaderBench (2)-individual assessment through reading strategies and textual complexity. Analyzing Discourse and Text Complexity for Learning and Collaborating (pp. 161–188). Springer International Publishing.
De Clercq, O., Hoste, V., Desmet, B., van Oosten, P., De Cock, M., & Macken, L. (2013). Using the crowd for readability prediction. Natural Language Engineering, 1(1). Cambridge University Press.Google Scholar logo with link to Google Scholar
Dell’Orletta, F., Montemagni, S. and Venturi, G. 2011. READ-IT: Assessing Readability of Italian Texts with a View to Text Simplification. In Proceedings of the 2nd Workshop on Speech and Language Processing for Assistive Technologies, (pp. 73–83).
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407. Google Scholar logo with link to Google Scholar
Duarte Torres, S., & Weber, I. (2011). What and how children search on the web. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM 2011) (pp. 393–402).
Feng, L., Elhadad, N., & Huenerfauth, M. (2009). Cognitively motivated features for readability assessment. Proceedings of the the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009) .
Ferguson, G., & Maclean, J. (1991). Assessing the readability of medical journal articles: An analysis of teacher judgements. Edinburgh Working Papers in Linguistics, 21, 112–125. [URL]Google Scholar logo with link to Google Scholar
Fernández Huerta, J. (1959). Medidas sencillas de lecturabilidad. Consigna, 2141, 29–32.Google Scholar logo with link to Google Scholar
Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233, Jun 1948. Google Scholar logo with link to Google Scholar
Flor, M., Klebanov, B.B., & Sheehan, K.M. (2013). Lexical tightness and text complexity. Proceedings of the Second Workshop on Natural Language Processing for Improving Textual Accessibility .
François, T.L. (2009). Combining a statistical language model with logistic regression to predict the lexical and syntactic difficulty of texts for FFL. Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop . Association for Computational Linguistics.
François, T., & Fairon, C. (2012). An AI readability formula for French as a foreign language. Proceedings of the 2012 Conference on Empirical Methods in Natural Language Processing (EMNLP 2012) (pp. 466–477).
François, T., & Miltsakaki, E. (2012). Do NLP and machine learning improve traditional readability formulas? Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations (pp. 49–57). Association for Computational Linguistics.
François, T., Brouwers, L., Naets, H., & Fairon, C. (2014). AMesure: une formule de lisibilité pour les textes administratifs. Actes de la 21e Conférence sur le Traitement automatique des Langues Naturelles (TALN 2014) (pp. 467–472). Marseille.
Fort, K., Adda, G., & Cohen, K.B. (2011). Amazon mechanical turk: Gold mine or coal mine? last words editorial. Computational Linguistics, 37(2). Google Scholar logo with link to Google Scholar
Fry, E. (1990). A readability formula for short passages. Journal of Reading, Vol. 33, No. 8, 594–597, May 1990.Google Scholar logo with link to Google Scholar
Gibson, E. (1998) Linguistic complexity: Locality of syntactic dependencies. Cognition, 681,1–76. Google Scholar logo with link to Google Scholar
Graesser, A.C., McNamara, D.S., Louwerse, M.M., & Cai, Z. (2004). Coh-metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, and Computers, 36(2), 193–202. Google Scholar logo with link to Google Scholar
Gyllstrom K., & Moens, M-F. (2010). Wisdom of the ages: Toward delivering the children’s web with the link-based agerank algorithm. Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM ’10) (pp. 159–168). ACM, New York, NY, USA.
Halliday, M.A.K., & Hasan, R. (1976). Cohesion in English. London: Longman.Google Scholar logo with link to Google Scholar
Hancke, J., Vajjala, S., & Meurers, D. (2012). Readability classification for German using lexical, syntactic, and morphological features. Proceedings of COLING 2012 (pp. 1063–1080).
Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2007). Combining lexical and grammatical features to improve readability measures for first and second language Texts. Proceedings of HLT-NAACL’07 (pp. 460–467).
Heilman, M., Collins-Thompson, K., & Eskenazi, M. (2008). An analysis of statistical models and features for reading difficulty prediction. Proceedings of the ACL 2008 BEA Workshop on Innovative Use of NLP for Building Educational Applications .
Heilman, M., Collins-Thompson, K., Eskenazi, M., Juffs, A., & Wilson, L. (2010). Personalization of reading passages improves vocabulary acquisition. International Journal of Artificial Intelligence in Education, 20(1).Google Scholar logo with link to Google Scholar
Honkela, T., Izzatdust, Z., & Lagus, K. (2012). Text mining for wellbeing: Selecting stories using semantic and pragmatic features. Artificial Neural Networks and Machine Learning–ICANN 2012 (pp. 467–474). Springer Berlin Heidelberg.
Jameel, S., Lam, W., & Qian, X. (2012). Ranking text documents on conceptual difficulty using term embedding and sequential discourse cohesion. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 145–152).
Kandel, L., & Moles, A. (1958). Application de l’Indice de Flesch à la langue français. Cahiers d’Etudes de Radio-Television, 191, 253–274.Google Scholar logo with link to Google Scholar
Kanungo, T., & Orr, D. (2009). Predicting the readability of short web summaries. Proceedings of the Second ACM International Conference on Web Search and Data Mining (WSDM ’09) (pp. 202–211). ACM, New York, NY, USA.
Kate, R.J., Luo, X., Patwardhan, S., Franz, M., Florian, R., Mooney, R.J., Roukos, S., & Welty, C. (2010). Learning to predict readability using diverse linguistic features. Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010) .
Kidwell, P., Lebanon, G., & Collins-Thompson, K. (2009). Statistical estimation of word acquisition with application to readability prediction. Proceedings of EMNLP’09 (pp. 900–909).
. (2011). Statistical estimation of word acquisition with application to readability prediction. Journal of the American Statistical Association, 106(493), 21–30. Google Scholar logo with link to Google Scholar
Kim, J.Y., Collins-Thompson, K., Bennett, P.N., & Dumais, S.T. (2012). Characterizing web content, user interests, and search behavior by reading level and topic. Proceedings of the fifth ACM International Conference on Web Search and Data Mining (WSDM ’12) (pp. 213–222). ACM, New York, NY, USA.
Kincaid, J.P., Fishburne, R.P., Rogers, R.L., & Chissom, B.S. (1975). Derivation of new readability formulas (Automated readability index, fog count, and flesch reading ease formula) for navy enlisted personnel. Research Branch Report 8–75. Chief of Naval Technical Training: Naval Air Station Memphis. Google Scholar logo with link to Google Scholar
Kireyev, K., & Landauer, T.K. (2011). Word maturity: Computational modeling of word knowledge. Proceedings of ACL 2011 (pp. 299–308).
Kittur, A., Chi, E.H., & Suh, B. (2008). Crowdsourcing user studies with mechanical turk. Proceedings of the 26th Annual ACM Conference on Human Factors in Computing Systems (CHI ‘08) (pp. 453–456). ACM.
Klare, G.R. (1963). The measurement of readability. Ames, IA: Iowa State University Press.Google Scholar logo with link to Google Scholar
Klerke, S., & Søgaard, A. DSim, a Danish Parallel Corpus for Text Simplification. In LREC May 2012 (pp. 4015–4018).
Landauer, T.K., Kireyev, K., & Panaccione, C. (2011). Word maturity: A new metric for word knowledge. Scientific Studies of Reading, 15(1), 92–108. Google Scholar logo with link to Google Scholar
Lau, T.P. (2006). Chinese readability analysis and its applications on the internet. CUHK, Masters Thesis, Hong Kong.
Lennon, C., & Burdick, H. (2004). The lexile framework as an approach for reading measurement and success. Technical Report. Metametrics, Inc. April 2004. [URL] (Retrieved Dec. 10, 2013)Google Scholar logo with link to Google Scholar
Malvern, D., & Richards, B. (2012). Measures of lexical richness. Encyclopedia of Applied Linguistics, Blackwell Publishing Ltd. Google Scholar logo with link to Google Scholar
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B, 42(2), 109–142.Google Scholar logo with link to Google Scholar
Mitchell, J.V. (1985). The ninth mental measurements yearbook. Lincoln, Nebraska: University of Nebraska Press.Google Scholar logo with link to Google Scholar
Nandhini, K., & Balasundaram, S.R. (2011). Improving readability of dyslexic learners through document summarization. Proceedings of the Technology for Education (T4E), 2011 IEEE International Conference on. IEEE (pp. 246–249).
Nelson, J., Perfetti, C., Liben, D., & Liben, M. (2012). Measures of text difficulty: Testing their predictive value for grade levels and student performance. Technical Report submitted to the Gates Foundation. Feb. 1, 2012. URL: [URL]
Paivio, A., Yuille, J.C., & Madigan, S.A. (1968). Concreteness, imagery, and meaningfulness: Values for 925 nouns. Journal of Experimental Psychology, 76(1), 1–25. Part 2 (1968). Google Scholar logo with link to Google Scholar
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1–135. Google Scholar logo with link to Google Scholar
Petersen, S.E. and Ostendorf, M. 2009. A machine learning approach to reading level assessment; In Computer Speech and Language, 231, (pp. 86–106). Google Scholar logo with link to Google Scholar
Pilán, I., Volodina, E., & Johansson, R. (2014). Rule-based and machine learning approaches for second language sentence-level readability. Proceedings of BEA Workshop 2014 .
Pitler, E., & Nenkova, A. (2008). Revisiting readability: A unified framework for predicting text quality. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP ’08) (pp. 186–195). Stroudsburg, PA, USA, Association for Computational Linguistics. [URL]
Rello, L., Saggion, H., Baeza-Yates, R., & Graells, E. (2012). Graphical schemes may improve readability but not understandability for people with dyslexia. Proceedings of NAACL-HLT 2012 .
Richardson, J.T.E. (1975). Imagery, concreteness, and lexical complexity, Quarterly Journal of Experimental Psychology, 27(2), 211–223. Psychology Press.Google Scholar logo with link to Google Scholar
Russell, D.M. (2011). SearchReSearch: Search by reading level [Web log post]. Retrieved from [URL]
Schwarm, S.E., & Ostendorf, M. (2005). Reading level assessment using support vector machines and statistical language models. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL ’05) (pp. 523–530). Stroudsburg, PA, USA, Association for Computational Linguistics.
Sato, S., Matsuyoshi, S., & Kondoh, Y. (2008). Automatic assessment of Japanese text readability based on a textbook corpus. Proceedings of LREC’08 .
Si, L., & Callan, J.P. (2001). A statistical model for scientific readability. Proceedings of CIKM’01 (pp. 574–576).
Sitbon, L., & Bellot, P. (2008). A readability measure for an information retrieval process adapted to dyslexics. Proceedings of the Second International Workshop on Adaptive Information Retrieval (AIR 2008) (pp. 52–57).
Sjöholm, J. (2012). Probability as readability: A new machine learning approach to readability assessment for written Swedish. Masters Thesis, Linköpings universitet, 2012.
Sung, Y.T., Chen, J.L., Cha, J.H., Tseng, H.C., Chang, T.H., & Chang, K.E. (2014). Constructing and validating readability models: The method of integrating multilevel linguistic features with machine learning. Behavior Research Methods, 2014 April 2, 1–15.Google Scholar logo with link to Google Scholar
Stenner, A.J., Burdick, H., Sanford, E.E., & Burdick, D.S. (2007). The lexile framework for reading technical report. MetaMetrics, Inc.Google Scholar logo with link to Google Scholar
Tan, C., Gabrilovich, E., & Pang, B. (2012). To each his own: Personalized content selection based on text comprehensibility. Proceedings of the 5th ACM International Conference on Web Search and Data Mining , February 2012.
Tanaka, S., Jatowt, A., Kato, M.P., & Tanaka, K. (2013). Estimating content concreteness for finding comprehensible documents. Proceedings of WSDM’13 . 475–484.
Tanaka-Ishii, K., Tezuka, S., & Terada, H. (2010). Sorting by readability. Computational Linguistics, 36(2), 203–227. Google Scholar logo with link to Google Scholar
Todirascu, A., François, T., Gala, N., Fairon, C., Ligozat, A.L., & Bernhard, D. (2013). Coherence and cohesion for the assessment of text readability. Natural Language Processing and Cognitive Science, 111, (pp. 11–19).Google Scholar logo with link to Google Scholar
Vapnik, V.N. (1995). The nature of statistical learning theory. New York: Springer-Verlag Inc. Google Scholar logo with link to Google Scholar
Vajjala, S., & Meurers, D. (2012). On improving the accuracy of readability classification using insights from second language acquisition. Proceedings of the Seventh Workshop on Building Educational Applications Using NLP (pp. 163–173). ACL.
von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8),58–67. (August 2008), Google Scholar logo with link to Google Scholar
Vor Der Brück, T., & Hartrumpf, S. (2007). A semantically oriented readability checker for German. Proceedings of the 3rd Language & Technology Conference (pp. 270–274). Poznan, Poland. October 2007.
Wang, Y. (2006). Automatic recognition of text difficulty from consumers health information. Proceedings of the IEEE Symposium on Computer-Based Medical Systems (pp. 131–136). Los Alamitos, CA, USA, IEEE Computer Society.
Zakaluk, B.L., & Samuels, S.J. (1988). Readability: Its past, present and future. International Reading Association. Newark, Del..Google Scholar logo with link to Google Scholar
Cited by (123)

Cited by 123 other publications

Crossley, Scott, Joon Suh Choi, Kenny Tang & Laurie Cutting
2026. The Tool for Automatic Analysis of Decoding Ambiguity (TAADA). Behavior Research Methods 58:2 DOI logo
Heijne, Jasper, Robin Ungruh & Maria Soledad Pera
2026. Blurred Lines: Understanding the Fit of Song Lyrics in Music Catalogs That Can Reach Children Through Recommendations. In Advances in Bias, Fairness, and Understudied Users in Information Retrieval [Communications in Computer and Information Science, 2786], ► pp. 60 ff. DOI logo
Mughal, Nimra, Ali Shariq Imran, Sher Muhammad Daudpota, Zenun Kastrati & Waheed Noor
2026. Exploring potential of large language models for automated essay scoring in education. Discover Artificial Intelligence 6:1 DOI logo
Parra, Verónica, Ana Corica & Daniela Godoy
2026. Insights on the Pedagogical Abilities of AI-Powered Tutors in Math Dialogues. Information 17:1 ► pp. 51 ff. DOI logo
Crossley, Scott A.
2025. Developing Linguistic Constructs of Text Readability Using Natural Language Processing. Scientific Studies of Reading 29:2 ► pp. 138 ff. DOI logo
Dharmavaram, Ashwin Ajov, Keerti Patil, Srivanth Srinivasan, Anvitha A Rao & M N Vijayakumar
2025. 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), ► pp. 1 ff. DOI logo
Djoudi, Anya Amel Nait, Patrice Bellot & Adrian-Gabriel Chifu
2025. Proceedings of the 2025 ACM Symposium on Document Engineering, ► pp. 1 ff. DOI logo
Elek-BenMoshe, Frida & Sheizaf Rafaeli
2025. Readability of online government information about welfare rights and benefits: the Israeli case. Universal Access in the Information Society 24:2 ► pp. 1659 ff. DOI logo
Elysia, Aurellia Gita & Yulyani Arifin
2025. 2025 5th International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), ► pp. 509 ff. DOI logo
Friedrich, Marcus C. G. & Elke Heise
2025. The Influence of Comprehensibility on Interest and Comprehension. Zeitschrift für Pädagogische Psychologie 39:1-2 ► pp. 139 ff. DOI logo
Friedrich, Marcus C. G. & Bianca Krenz
2025. Comprehensibility of gender-fair language in German-language video lectures. Frontiers in Psychology 15 DOI logo
Jain, Yoshee, John Hollander, Amber He, Sunny Tang, Liang Zhang & John Sabatini
2025. Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty. In Adaptive Instructional Systems [Lecture Notes in Computer Science, 15812], ► pp. 202 ff. DOI logo
Liu, Fengkai, Tan Jin & John S. Y. Lee
2025. Automatic readability assessment for sentences: neural, hybrid and large language models. Language Resources and Evaluation 59:3 ► pp. 2265 ff. DOI logo
Mesmer, Heidi Anne E.
2025. Exploring the frequency contours in close reading texts. Reading and Writing 38:1 ► pp. 1 ff. DOI logo
Moreira, Raquel
2025. Readability, Fluency and Error identification when using Machine Translation and AI to translate medical research texts. Langues & Parole 10 ► pp. 179 ff. DOI logo
Nahatame, Shingo & Kazuhiro Yamaguchi
2025. Revisiting Text Readability and Processing Effort in Second Language Reading: Bayesian Analysis of Eye‐Tracking Data. Language Learning DOI logo
Pan, Wenjing, Xia Li, Xiaoyin Chen & Rui Xu
2025. Textual form features for text readability assessment. Natural Language Processing 31:3 ► pp. 800 ff. DOI logo
Peters, Pam
2025. 133Online terminography for Indigenous Australians. In Regarding the Significance of Lexicography / Zur Bedeutsamkeit der Lexikographie, ► pp. 133 ff. DOI logo
van der Sluis, Frans & Egon L. van den Broek
2025. Model interpretability enhances domain generalization in the case of textual complexity modeling. Patterns 6:2 ► pp. 101177 ff. DOI logo
Zhang, Xiaopeng & Xiaofei Lu
2025. Aligning linguistic complexity with the difficulty of English texts for L2 learners based on CEFR levels. Studies in Second Language Acquisition 47:5 ► pp. 1407 ff. DOI logo
Bhogayata, Chandrakant K.
2024. A Machine Learning–Based Readability Model for Gujarati Texts. ACM Transactions on Asian and Low-Resource Language Information Processing 23:2 ► pp. 1 ff. DOI logo
Chen, Chen & Philip Durrant
2024. Lexical complexity in academic lectures: Comparative analysis of EMI and Non-EMI settings and influential factors. Applied Corpus Linguistics 4:3 ► pp. 100115 ff. DOI logo
Friedrich, Marcus C. G., Selina Gajewski, Katja Hagenberg, Christine Wenz & Elke Heise
2024. Does the gender asterisk (“Gendersternchen”) as a special form of gender-fair language impair comprehensibility?. Discourse Processes 61:9 ► pp. 439 ff. DOI logo
Iavarone, Benedetta, Maria Sole Morelli, Dominique Brunato, Shadi Ghiasi, Enzo Pasquale Scilingo, Nicola Vanello, Felice Dell’Orletta & Alberto Greco
2024. The linguistic structure of an emotional text influences the sympathetic activity and the speech prosody of the reader. Biomedical Signal Processing and Control 89 ► pp. 105776 ff. DOI logo
Martinez-Gil, Jorge
2024. Optimizing readability using genetic algorithms. Knowledge-Based Systems 284 ► pp. 111273 ff. DOI logo
Mozer, Reagan, Luke Miratrix, Jackie Eunjung Relyea & James S. Kim
2024. Combining Human and Automated Scoring Methods in Experimental Assessments of Writing: A Case Study Tutorial. Journal of Educational and Behavioral Statistics 49:5 ► pp. 780 ff. DOI logo
Uchida, Satoru, Yuki Arase & Tomoyuki Kajiwara
2024. Profiling English sentences based on CEFR levels. ITL - International Journal of Applied Linguistics 175:1 ► pp. 103 ff. DOI logo
Yusuf, Wasiu, Fausat Alesinloye, Ebunoluwa Oyegoke & Amina Tambaya
2024. Determinants of E-Commerce: A Case of Fashion Wears and Cosmetics E-Businesses in Nigeria. Open Journal of Social Sciences 12:01 ► pp. 374 ff. DOI logo
Abu Doush, Iyad, Khalid Sultan, Mohammed Azmi Al-Betar, Zainab Almeraj, Zaid Abdi Alkareem Alyasseri & Mohammed A. Awadallah
2023. Web accessibility automatic evaluation tools: to what extent can they be automated?. CCF Transactions on Pervasive Computing and Interaction 5:3 ► pp. 288 ff. DOI logo
Brunato, Dominique & Giulia Venturi
2023. Why is this language complex? Cherry-pick the optimal set of features in multilingual treebanks. Linguistics Vanguard 9:s1 ► pp. 59 ff. DOI logo
Chung, Andy & Kumiko Tanaka-Ishii
2023. 4th ACM International Conference on AI in Finance , ► pp. 401 ff. DOI logo
Dodson, Samuel, Luanne Sinnamon & Rick Kopak
2023. Spontaneous Learning Environments: Manipulating Readability & Cohesion in Support of Searching as Learning. Proceedings of the Association for Information Science and Technology 60:1 ► pp. 570 ff. DOI logo
Durward, Matthew & Peter Organisciak
2023. Enriching Library Holdings for English Language Learners: Applying Traditional Readability Formulas and Modern Cohesion Methods to Film. Proceedings of the Association for Information Science and Technology 60:1 ► pp. 121 ff. DOI logo
Higasa, Taichi, Keitaro Tanaka, Qi Feng & Shigeo Morishima
2023. International Cconference on Multimodal Interaction , ► pp. 292 ff. DOI logo
Higasa, Taichi, Keitaro Tanaka, Qi Feng & Shigeo Morishima
2024. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, ► pp. 1 ff. DOI logo
Jalongo, Mary Renck
2023. Extending Your Academic Footprint: Readability, Originality, and Altmetrics. In Scholarly Writing [Springer Texts in Education, ], ► pp. 323 ff. DOI logo
Kong, Nancy, Uwe Dulleck, Adam B. Jaffe, Shupeng Sun & Sowmya Vajjala
2023. Linguistic metrics for patent disclosure: Evidence from university versus corporate patents. Research Policy 52:2 ► pp. 104670 ff. DOI logo
Medardo Tapia-Téllez, José, Aurelio López-López, Samuel González-López & Jesús Miguel García-Gorrostieta
2023. Readability Assessment of Academic Texts at Different Degree Levels. In Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference [Lecture Notes in Networks and Systems, 580], ► pp. 37 ff. DOI logo
Montefinese, Maria, Lorenzo Gregori, Andrea Amelio Ravelli, Rossella Varvara, Daniele Paolo Radicioni & Yiu-Kei Tsang
2023. CONcreTEXT norms: Concreteness ratings for Italian and English words in context. PLOS ONE 18:10 ► pp. e0293031 ff. DOI logo
Nahatame, Shingo
2023. Predicting processing effort during L1 and L2 reading: The relationship between text linguistic features and eye movements. Bilingualism: Language and Cognition 26:4 ► pp. 724 ff. DOI logo
Nouri, Zahra, Nikhil Prakash, Ujwal Gadiraju & Henning Wachsmuth
2023. Proceedings of the 28th International Conference on Intelligent User Interfaces, ► pp. 737 ff. DOI logo
Park, Min Su & Eunil Park
2023. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, ► pp. 1233 ff. DOI logo
Pickelmann, Florian, Michael Färber & Adam Jatowt
2023. Ablesbarkeitsmesser: A System for Assessing the Readability of German Text. In Advances in Information Retrieval [Lecture Notes in Computer Science, 13982], ► pp. 288 ff. DOI logo
Saha, Sangita, Apurbalal Senapati & Ranjan Maity
2023. 2023 IEEE Guwahati Subsection Conference (GCON), ► pp. 01 ff. DOI logo
Sandnes, Frode Eika & Evelyn Eika
2023. Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, ► pp. 237 ff. DOI logo
Sibeko, Johannes & Menno van Zaanen
2023. A Data Set of Final Year High School Examination Texts of South African Home and First Additional Language Subjects. Journal of Open Humanities Data 9 DOI logo
Yin, Xiaojun, Guolong Song & Hongbin Wang
2023. Proceedings of the 2023 6th International Conference on Big Data and Education, ► pp. 118 ff. DOI logo
Zhang, Jiajin, Zhijuan Wang & Xiaobin Zhao
2023. 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), ► pp. 1224 ff. DOI logo
Alim, Sophia & Abid Ismail
2022. An Initial Exploration of Tweets Associated With Web Accessibility. International Journal of Art, Culture, Design, and Technology 11:1 ► pp. 1 ff. DOI logo
Blinova, Olga & Nikita Tarasov
2022. A hybrid model of complexity estimation: Evidence from Russian legal texts. Frontiers in Artificial Intelligence 5 DOI logo
Crossley, Scott, Aron Heintz, Joon Suh Choi, Jordan Batchelor, Mehrnoush Karimi & Agnes Malatinszky
2022. A large-scaled corpus for assessing text readability. Behavior Research Methods 55:2 ► pp. 491 ff. DOI logo
François, Thomas & Marie-Aude Lefer
2022. Revisiting simplification in corpus-based translation studies: Insights from readability research. Meta 67:1 ► pp. 50 ff. DOI logo
Ilgen, Bahar & Chris Biemann
2022. Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval, ► pp. 157 ff. DOI logo
Koc, Kerim & Asli Pelin Gurgun
2022. Ambiguity factors in construction contracts entailing conflicts. Engineering, Construction and Architectural Management 29:5 ► pp. 1946 ff. DOI logo
Koc, Kerim & Asli Pelin Gurgun
2021. Assessment of Readability Risks in Contracts Causing Conflicts in Construction Projects. Journal of Construction Engineering and Management 147:6 DOI logo
Limbud, Charkkri, Yen-Hao Huang, Alejandro Cortes & Yi-Shin Chen
2022. 2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), ► pp. 101 ff. DOI logo
Lin, Jionghao, Mladen Rakovic, David Lang, Dragan Gasevic & Guanliang Chen
2022. LAK22: 12th International Learning Analytics and Knowledge Conference, ► pp. 282 ff. DOI logo
Lin, Jionghao, Shaveen Singh, Lele Sha, Wei Tan, David Lang, Dragan Gašević & Guanliang Chen
2022. Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems 127 ► pp. 194 ff. DOI logo
Olney, Andrew M.
2022. Assessing Readability by Filling Cloze Items with Transformers. In Artificial Intelligence in Education [Lecture Notes in Computer Science, 13355], ► pp. 307 ff. DOI logo
Sharoff, Serge Aleksandrovich
2022. What neural networks know about linguistic complexity. Russian Journal of Linguistics 26:2 ► pp. 371 ff. DOI logo
Solnyshkina, Marina Ivanovna, Danielle S. McNamara & Radif Rifkatovich Zamaletdinov
2022. Natural language processing and discourse complexity studies. Russian Journal of Linguistics 26:2 ► pp. 317 ff. DOI logo
Tapia-Téllez, José Medardo, Aurelio López-López & Samuel González-López
2022. Comprehensibility Analysis and Assessment of Academic Texts. In Artificial Intelligence in Education: Emerging Technologies, Models and Applications [Lecture Notes on Data Engineering and Communications Technologies, 104], ► pp. 51 ff. DOI logo
Zhang, Haomin, Yuting Han, Xing Zhang & Liuran Cui
2022. Frequency, Dispersion and Abstractness in the Lexical Sophistication Analysis of A Learner-Based Word Bank: Dimensionality Reduction and Identification. Journal of Quantitative Linguistics 29:2 ► pp. 195 ff. DOI logo
Baazeem, Ibtehal, Hend Al-Khalifa & Abdulmalik Al-Salman
2021. Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking. Applied Sciences 11:18 ► pp. 8607 ff. DOI logo
Baazeem, Ibtehal, Hend Al-Khalifa & Abdulmalik Al-Salman
2025. AraEyebility: Eye-Tracking Data for Arabic Text Readability. Computation 13:5 ► pp. 108 ff. DOI logo
Baazeem, Ibtehal, Hend Al-Khalifa & Abdulmalik Al-Salman
2025. Integrating Linguistic and Eye Movements Features for Arabic Text Readability Assessment Using ML and DL Models. Computation 13:11 ► pp. 258 ff. DOI logo
Huynh, Larry, Thai Nguyen, Joshua Goh, Hyoungshick Kim & Jin B. Hong
2021. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ► pp. 3847 ff. DOI logo
Karuna, Prakruthi, Hemant Purohit, Sushil Jajodia, Rajesh Ganesan & Ozlem Uzuner
2021. Fake Document Generation for Cyber Deception by Manipulating Text Comprehensibility. IEEE Systems Journal 15:1 ► pp. 835 ff. DOI logo
Liu, Qingxia, Gong Cheng, Kalpa Gunaratna & Yuzhong Qu
2021. Entity summarization: State of the art and future challenges. Journal of Web Semantics 69 ► pp. 100647 ff. DOI logo
Madreiter, Theresa, Linus Kohl & Fazel Ansari
2021. A Text Understandability Approach for Improving Reliability-Centered Maintenance in Manufacturing Enterprises. In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems [IFIP Advances in Information and Communication Technology, 630], ► pp. 161 ff. DOI logo
Malinova, Monika & Jan Mendling
2021. Cognitive Diagram Understanding and Task Performance in Systems Analysis and Design. MIS Quarterly 45:4 ► pp. 2101 ff. DOI logo
Martinc, Matej, Senja Pollak & Marko Robnik-Šikonja
2021. Supervised and Unsupervised Neural Approaches to Text Readability. Computational Linguistics 47:1 ► pp. 141 ff. DOI logo
Nouri, Zahra, Ujwal Gadiraju, Gregor Engels & Henning Wachsmuth
2021. Proceedings of the 32st ACM Conference on Hypertext and Social Media, ► pp. 165 ff. DOI logo
Ojha, Pawan Kumar, Abid Ismail & Kuppusamy Kundumani Srinivasan
2021. Perusal of readability with focus on web content understandability. Journal of King Saud University - Computer and Information Sciences 33:1 ► pp. 1 ff. DOI logo
Omar, Salehah, Juhaida Abu Bakar, Maslinda Mohd Nadzir, Nor Hazlyna Harun & Nooraini Yusoff
2021. 2021 International Conference on Computer & Information Sciences (ICCOINS), ► pp. 345 ff. DOI logo
Peters, Pam & Jan-Louis Kruger
2021. The readability of online health information for L1 and L2 Australians: text-based and user-focused research. Text & Talk 41:5-6 ► pp. 787 ff. DOI logo
Qiang, Jipeng, Xinyu Lu, Yun Li, Yunhao Yuan & Xindong Wu
2021. Chinese Lexical Simplification. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 ► pp. 1819 ff. DOI logo
Vanroy, Bram, Orphée De Clercq, Arda Tezcan, Joke Daems & Lieve Macken
2021. Metrics of Syntactic Equivalence to Assess Translation Difficulty. In Explorations in Empirical Translation Process Research [Machine Translation: Technologies and Applications, 3], ► pp. 259 ff. DOI logo
Balyan, Renu, Kathryn S. McCarthy & Danielle S. McNamara
2020. Applying Natural Language Processing and Hierarchical Machine Learning Approaches to Text Difficulty Classification. International Journal of Artificial Intelligence in Education 30:3 ► pp. 337 ff. DOI logo
Banks, Amanda, Christopher J. White, Casey Eaton & Bryan Mesmer
2020. ASCEND 2020, DOI logo
Bhardwaj, Anupam, Pooja Khanna, Sachin Kumar & Pragya
2020. Generative Model for NLP Applications based on Component Extraction. Procedia Computer Science 167 ► pp. 918 ff. DOI logo
Buongiovanni, Chiara, Francesco Gracci, Dominique Brunato & Felice Dell’Orletta
2020. Lost in Text: A Cross-Genre Analysis of Linguistic Phenomena within Text. Italian Journal of Computational Linguistics 6:1 ► pp. 47 ff. DOI logo
Clinton, Virginia, Terrill Taylor, Surjya Bajpayee, Mark L. Davison, Sarah E. Carlson & Ben Seipel
2020. Inferential comprehension differences between narrative and expository texts: a systematic review and meta-analysis. Reading and Writing 33:9 ► pp. 2223 ff. DOI logo
Meng, Changping, Muhao Chen, Jie Mao & Jennifer Neville
2020. ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis. In Advances in Information Retrieval [Lecture Notes in Computer Science, 12035], ► pp. 33 ff. DOI logo
Pecout, Anaïs, Thi Mai Tran & Natalia Grabar
2020. Améliorer la diffusion de l’information sur la maladie d’Alzheimer : étude pilote sur la simplification de textes médicaux. Éla. Études de linguistique appliquée N° 195:3 ► pp. 325 ff. DOI logo
Santini, Marina & Arne Jönsson
2020. Pinning down text complexity. Register Studies 2:2 ► pp. 306 ff. DOI logo
Antunes, Helder & Carla Teixeira Lopes
2019. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), ► pp. 1 ff. DOI logo
Antunes, Hélder & Carla Teixeira Lopes
2019. Analyzing the Adequacy of Readability Indicators to a Non-English Language. In Experimental IR Meets Multilinguality, Multimodality, and Interaction [Lecture Notes in Computer Science, 11696], ► pp. 149 ff. DOI logo
Bilal, Dania & Li-Min Huang
2019. Readability and word complexity of SERPs snippets and web pages on children’s search queries. Aslib Journal of Information Management 71:2 ► pp. 241 ff. DOI logo
Chen, Guanliang, Jie Yang & Dragan Gasevic
2019. A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation. In Artificial Intelligence in Education [Lecture Notes in Computer Science, 11625], ► pp. 59 ff. DOI logo
Chen, Xiaobin & Detmar Meurers
2019. Linking text readability and learner proficiency using linguistic complexity feature vector distance. Computer Assisted Language Learning 32:4 ► pp. 418 ff. DOI logo
Ismail, Abid, K.S. Kuppusamy, Ajit Kumar & Pawan Kumar Ojha
2019. Connect the dots: Accessibility, readability and site ranking – An investigation with reference to top ranked websites of Government of India. Journal of King Saud University - Computer and Information Sciences 31:4 ► pp. 528 ff. DOI logo
Jiang, Zhiwei, Qing Gu, Yafeng Yin, Jianxiang Wang & Daoxu Chen
2019. GRAW+: A two‐view graph propagation method with word coupling for readability assessment. Journal of the Association for Information Science and Technology 70:5 ► pp. 433 ff. DOI logo
Malyuga, E. N.
2019. SYNTACTIC CHARACTERISTICS OF ADVERTISING DISCOURSE. Vestnik of Samara University. History, pedagogics, philology 25:4 ► pp. 100 ff. DOI logo
Matricciani, Emilio
2019. Deep Language Statistics of Italian throughout Seven Centuries of Literature and Empirical Connections with Miller’s 7 ∓ 2 Law and Short-Term Memory. Open Journal of Statistics 09:03 ► pp. 373 ff. DOI logo
Matricciani, Emilio
2020. A Statistical Theory of Language Translation Based on Communication Theory. Open Journal of Statistics 10:06 ► pp. 936 ff. DOI logo
Matricciani, Emilio
2023. Readability across Time and Languages: The Case of Matthew’s Gospel Translations. AppliedMath 3:2 ► pp. 497 ff. DOI logo
Matricciani, Emilio
2023. Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations. Open Journal of Statistics 13:03 ► pp. 379 ff. DOI logo
Matricciani, Emilio
2023. Readability Indices Do Not Say It All on a Text Readability. Analytics 2:2 ► pp. 296 ff. DOI logo
Palotti, Joao, Guido Zuccon & Allan Hanbury
2019. Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms. Journal of Medical Internet Research 21:1 ► pp. e10986 ff. DOI logo
Tseng, Hou-Chiang, Berlin Chen, Tao-Hsing Chang & Yao-Ting Sung
2019. Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts. Natural Language Engineering 25:3 ► pp. 331 ff. DOI logo
Tseng, Hou-Chiang, Hsueh-Chih Chen, Kuo-En Chang, Yao-Ting Sung & Berlin Chen
2019. An Innovative BERT-Based Readability Model. In Innovative Technologies and Learning [Lecture Notes in Computer Science, 11937], ► pp. 301 ff. DOI logo
Vanroy, Bram, Orphée De Clercq & Lieve Macken
2019. Correlating process and product data to get an insight into translation difficulty. Perspectives 27:6 ► pp. 924 ff. DOI logo
Dalvean, Michael Coleman & Galbadrakh Enkhbayar
2018. A New Text Readability Measure for Fiction Texts. SSRN Electronic Journal DOI logo
Ferrari, Alessio, Gloria Gori, Benedetta Rosadini, Iacopo Trotta, Stefano Bacherini, Alessandro Fantechi & Stefania Gnesi
2018. Detecting requirements defects with NLP patterns: an industrial experience in the railway domain. Empirical Software Engineering 23:6 ► pp. 3684 ff. DOI logo
Ferro, Marcello, Claudia Cappa, Sara Giulivi, Claudia Marzi, Oaufae Nahli, Franco Alberto Cardillo & Vito Pirrelli
2018. 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), ► pp. 1 ff. DOI logo
Fuhr, Norbert, Anastasia Giachanou, Gregory Grefenstette, Iryna Gurevych, Andreas Hanselowski, Kalervo Jarvelin, Rosie Jones, YiquN Liu, Josiane Mothe, Wolfgang Nejdl, Isabella Peters & Benno Stein
2018. An Information Nutritional Label for Online Documents. ACM SIGIR Forum 51:3 ► pp. 46 ff. DOI logo
Michael Dalvean & Galbadrakh Enkhbayar
2018. Assessing the readability of fiction: a corpus analysis and readability ranking of 200 English fiction texts. Linguistic Research 35:null ► pp. 137 ff. DOI logo
Thoms, Brian, Evren Eryilmaz, Nicole Dubin, Rafael Hernandez & Sara Colon-Cerezo
2018. 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), ► pp. 329 ff. DOI logo
Thoms, Brian, Evren Eryilmaz, Nicole Dubin, Rafael Hernandez & Sara Colon-Cerezo
2020. Real-time visualization to improve quality in computer mediated communication. Web Intelligence 18:1 ► pp. 1 ff. DOI logo
Al Khalil, Muhamed, Nizar Habash & Hind Saddiki
2017. Simplification of Arabic Masterpieces for Extensive Reading: A Project Overview. Procedia Computer Science 117 ► pp. 192 ff. DOI logo
Dascalu, Mihai, Philippe Dessus, Laurent Thuez & Stefan Trausan-Matu
2017. How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. In Data Driven Approaches in Digital Education [Lecture Notes in Computer Science, 10474], ► pp. 43 ff. DOI logo
Ferrari, Alessio, Felice Dell'Orletta, Andrea Esuli, Vincenzo Gervasi & Stefania Gnesi
2017. Natural Language Requirements Processing: A 4D Vision. IEEE Software 34:6 ► pp. 28 ff. DOI logo
Gadiraju, Ujwal, Jie Yang & Alessandro Bozzon
2017. Proceedings of the 28th ACM Conference on Hypertext and Social Media, ► pp. 5 ff. DOI logo
Pires, Carla, Afonso Cavaco & Marina Vigário
2017. Towards the Definition of Linguistic Metrics for Evaluating Text Readability. Journal of Quantitative Linguistics 24:4 ► pp. 319 ff. DOI logo
Saddiki, Hind, Violetta Cavalli-Sforza & Karim Bouzoubaa
2017. Enhancing Visualization in Readability Reports for Arabic Texts. Procedia Computer Science 117 ► pp. 241 ff. DOI logo
Sheehan, Kathleen M.
2017. Validating Automated Measures of Text Complexity. Educational Measurement: Issues and Practice 36:4 ► pp. 35 ff. DOI logo
Bilal, Dania & Jacek Gwizdka
2016. Children's eye‐fixations on google search results. Proceedings of the Association for Information Science and Technology 53:1 ► pp. 1 ff. DOI logo
De Clercq, Orphée & Véronique Hoste
2016. All Mixed Up? Finding the Optimal Feature Set for General Readability Prediction and Its Application to English and Dutch. Computational Linguistics 42:3 ► pp. 457 ff. DOI logo
Tanaka, Shotaro, Adam Jatowt & Katsumi Tanaka
2016. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), ► pp. 256 ff. DOI logo
Gkotsis, George, Maria Liakata, Carlos Pedrinaci, Karen Stepanyan & John Domingue
2015. ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features. The Journal of Web Science 1:1 ► pp. 1 ff. DOI logo
Saddiki, Hind, Karim Bouzoubaa & Violetta Cavalli-Sforza
2015. 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), ► pp. 1 ff. DOI logo
[no author supplied]
2017. Automatic Text Simplification [Synthesis Lectures on Human Language Technologies, ], DOI logo

This list is based on CrossRef data as of 30 march 2026. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue