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. 97135
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
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
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. DOI logoGoogle 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
Benjamin, R. (2012). Reconstructing readability: Recent developments and recommendations in the analysis of text difficulty. Educational Psychology Review, 24(1), 63–88. DOI logoGoogle Scholar
Carroll, J.B., Davies, P., & Richman, B. (1971). Word frequency book. Boston: Houghton Mifflin.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
Chall, J.S., & Dale, E. (1995). Readability revisited: The New Dale-Chall readability formula. Cambridge, MA: Brookline Books.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
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
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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Dale, E., & Chall, J.S. (1949). The concept of readability. Consciousness and Cognition, 26(23).Google Scholar
Dale, E., & O’Rourke, J. (1981). The living word vocabulary. Chicago, IL: World Book/Childcraft International.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 . DOI logo
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
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. DOI logoGoogle 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) . DOI logo
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
Fernández Huerta, J. (1959). Medidas sencillas de lecturabilidad. Consigna, 2141, 29–32.Google Scholar
Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233, Jun 1948. DOI logoGoogle 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. DOI logo
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). DOI logoGoogle Scholar
Fry, E. (1990). A readability formula for short passages. Journal of Reading, Vol. 33, No. 8, 594–597, May 1990.Google Scholar
Gibson, E. (1998) Linguistic complexity: Locality of syntactic dependencies. Cognition, 681,1–76. DOI logoGoogle 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. DOI logoGoogle 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
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 . DOI logo
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
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
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. DOI logoGoogle 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. DOI logoGoogle 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
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. DOI logoGoogle 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
Malvern, D., & Richards, B. (2012). Measures of lexical richness. Encyclopedia of Applied Linguistics, Blackwell Publishing Ltd. DOI logoGoogle Scholar
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B, 42(2), 109–142.Google Scholar
Mitchell, J.V. (1985). The ninth mental measurements yearbook. Lincoln, Nebraska: University of Nebraska Press.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). DOI logoGoogle Scholar
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1–135. DOI logoGoogle 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). DOI logoGoogle 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 . DOI logo
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
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
Stenner, A.J., Burdick, H., Sanford, E.E., & Burdick, D.S. (2007). The lexile framework for reading technical report. MetaMetrics, Inc.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. DOI logoGoogle 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
Vapnik, V.N. (1995). The nature of statistical learning theory. New York: Springer-Verlag Inc. DOI logoGoogle 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.
. (2014). Readability assessment for text simplification: From analyzing documents to identifying sentential simplifications. ITL International Journal of Applied Linguistics, Sept. 2014. DOI logoGoogle Scholar
von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8),58–67. (August 2008), DOI logoGoogle 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
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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
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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
Bilal, Dania & Yan Zhang
2021. Teens’ Conceptual Understanding of Web Search Engines: The Case of Google Search Engine Result Pages (SERPs). In Human-Computer Interaction. Design and User Experience Case Studies [Lecture Notes in Computer Science, 12764],  pp. 253 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
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
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
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

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