Contents

• Early Research (1940s to early 2000s)
• Recent Formula based and other non-learning approaches
• Corpora Creation
• Language Modeling, Machine Learning approaches
• User studies, Eye tracking experiments etc.,
• Cognitive and Psycholinguistic perspectives on readability
• Tools
• Applying Readability Assessment for various scenarios
• Corpus studies, evaluations, discussions etc.
• Various Theses on Readability Assessment
• Focused workshops on this topic and related topics

Early research (1940s to early 2000s)

• Dale, E. and Chall, J. S.; A Formula for Predicting Readability Educational research bulletin; Organ of the College of Education, 1948, 27, 11-28.
• Flesch, R. F.; A New Readability Yardstick; Journal of Applied Psychology, 1948, 32, 221-233.
• Klare, G. R.; The Measurement of Readability; Iowa State University Press, 1963.
• Smith, E. A. and Senter, R. J.; Automated Readability Index; Aerospace Medical Research Laboratories, 1967.
• McLaughlin, G. H.; SMOG Grading -- a New Readability Formula; Journal of Reading, 1969, 12, 639-646.
• McLaughlin, G. H.; Temptations of the Flesch; Instructional Science, 1974, 2, 367-384.
• Coleman, M. and Liau, T. L.; A Computer Readability Formula Designed for Machine Scoring; Journal of Applied Psychology, 1975, 60, 283-284.
• Kincaid, J. P.; Fishburne, R. P. J.; Rogers, R. L. and Chissom, B. S.; Derivation of new readability formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy enlisted personnel; Naval Technical Training Command, 1975
• Harris, A. J. and Jacobson, M. D. A Comparison of the Fry, Spache, and Harris-Jacobson Readability Formulas for Primary Grades; The Reading Teacher, International Reading Association, 1980, 33, 920-924
• Fry, E.; Comments on the Preceding Harris and Jacobson Comparison of the Fry, Spache, and Harris-Jacobson Readability Formulas; The Reading Teacher, International Reading Association, 1980, 33, 924-926.
• Stevens, K. C.; Readability Formulae and McCall-Crabbs Standard Test Lessons in Reading; The Reading Teacher, International Reading Association, 1980, 33, 413-415.
• McCallum, D. R. and Peterson, J. L.; Computer-based readability indexes; Proceedings of the ACM '82 conference, 1982, 44-48.
• Anderson, J.; Lix and Rix: Variations on a Little-Known Readability Index; Journal of Reading, 1983, 26, 490-496.
• Franchina, V. and Vacca, R.; Adaptation of Flesch readability index on a bilingual text written by the same author both in Italian and English languages; Linguaggi, Linguaggi, 1986, 47-49.
• Guillemette, R. A.; Predicting readability of data processing written materials; ACM SIGMIS Database, 1987, 18, 40-47.
• Tham, T. M.; Linguistic variables as predictors of Chinese text readability National University of Singapore, 1987. (Chinese)
• Yuka, T.; Yoshihiko, O. and Hisao, Y.; A computer readability formula of Japanese texts for machine scoring; Proceedings of the 12th conference on Computational linguistics - Volume 2, Association for Computational Linguistics, 1988, 649-654. (Japanese)
• Chall, J. S. and Dale, E.; Readability Revisted: The New Dale-Chall Readability Formula; Brookline Books, 1995.
• Stenner, A. J.; Measuring reading comprehension with the Lexile framework; Fourth North American Conference on Adolescent/Adult Literacy, 1996.
• Crossley, S. A.; Dufty, D. F.; McCarthy, P. M. and Mcnamara, D. S.; Toward a New Readability : A Mixed Model Approach; Proceedings of the 29th annual conference of the Cognitive Science Society. Austin, TX, Cognitive Science Society, 2000, 197-202.
• Klare, G. R.; The measurement of readability: useful information for communicators; ACM Journal of Computer Documentation, ACM, 2000, 24, 107-121.
• Redish, J.; Readability formulas have even more limitations than Klare discusses; ACM J. Comput. Doc., ACM, 2000, 24, 132-137.
• Nezhad, G. R. H. P.; Reading complexity judgments: Episode 1; Shiken: JALT Testing and Evaluation SIG Newsletter, 2001, 5, 2-5.
• Nezhad, G. R. H. P.; Reading complexity judgments: Episode 2 Shiken: JALT Testing and Evaluation SIG Newsletter, 2002, 6, 7-13.
• Nezhad, G. R. H. P.; Reading complexity judgments: Episode 3 Shiken: JALT Testing and Evaluation SIG Newsletter, 2002, 6, 8-11.
• McNamara, D. S.; Louwerse, M. M. and Graesser, A. C.; Coh-Metrix: Automated Cohesion and Coherence Scores to Predict Text Readability and Facilitate Comprehension; 2002.
• DuBay, W. H.; The Principles of Readability; Impact Information, 2004.
• Ken, N.; In Search of New Dimensions for Readability for Japanese Learners of English; Shizuoka Sangyo University, 2004.

More recent formula based and other non-learning approaches, User studies

• Bennöhr, J.; A Web-based Personalised Textfinder for Language Learners; School of Informatics, University of Edinburgh, 2005.
• Uitdenbogerd, A. L.; Readability of French as a Foreign Language and its Uses; Proceedings of the 10th Australasian Document Computing Symposium, 2005. (French)
• Anagnostou, N. K. and Weir, G. R. S.; From corpus-based collocation frequencies to readability measure ;I CT in the Analysis, Teaching and Learning of Languages, Preprints of the ICTATLL Workshop 2006, 33-46.
• Campbell, G. and Weir, G.; Matching Readers to Texts with the Strathclyde Readability Measure; ICT in the Analysis, Teaching and Learning of Languages, Preprints of the ICTATLL Workshop 2006, 59-64.
• Weir, G. R. S. and Ritchie, C.; Estimating Readability with the Strathclyde Readability Measure; ICT in the Analysis, Teaching and Learning of Languages, Preprints of the ICTATLL Workshop 2006., 2006, 25-32.
• Ozasa, T.; Weir, G. R. S. and Fukui, M.; Measuring Readability for Japanese Learners of English; Proceedings of the 12th Conference of Pan-Pacific Association of Applied Linguistics, 2007. (Japanese)
• Crossley, S. A.; Greenfield, J. and McNamara, D. S.; Assessing Text Readability Using Cognitively Based Indices; Teachers of English to Speakers of Other Languages, Inc. 700 South Washington Street Suite 200, Alexandria, VA 22314, 2008, 475-493.
• Ozasa, T.; Weir, G. and Fukui, M.; Toward a Readability Index for Japanese Learners of EFL; Proceedings of the 13th Conference of Pan-Pacific Association of Applied Linguistics (PAAL'08), Pan-Pacific Association of Applied Linguistics, 2008
• Sitbon, L. and Bellot, P.; A readability measure for an information retrieval process adapted to dyslexics; In Second international workshop on Adaptive Information Retrieval (AIR), 2008. (French)
• Weir, G. R. S. and Anagnostou, N. K.; Collocation frequency as a readability factor; Proceedings of the 13th Conference of Pan-Pacific Association of Applied Linguistics , 2008.
• Newbold, N. and Gillam, L.; The Linguistics of Readability: The Next Step for Word Processing; Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing, Association for Computational Linguistics, 2010
• Sinha, M.; Sharma, S.; Dasgupta, T. and Basu, A. New Readability Measures for Bangla and Hindi Texts Proceedings of COLING 2012. (Bangla, Hindi)
• Gondy Leroy and David Kauchak; The effect of word familiarity on actual and perceived text difficulty; Journal of the American medical informatics association, 2013.

Corpora creation

• Miller, G. and Coleman, E. ; A Set of thirty six passages calibrated for complexity; Journal of Verbal Learning and Verbal Behavior, 1967, 6(6), 851-854
• Britton, B. K.; Dusen, L. V.; Gülgöz, S. and Glynn, S. M.; Instructional Texts Rewritten by Five Expert Teams: Revisions and Retention; Improvements Journal of Educational Psychology, 1989, 81, 226-239.
• Britton, B. K.; Dusen, L. V. and Gülgöz, S.; Reply to "A Response to 'Instructional Texts Rewritten by Five Expert Teams"'; Journal of Educational Psychology, 1991, 83, 149-152
• Kandula, S. and Zeng-Treitler, Q.; Creating a Gold Standard for the Readability Measurement of Health Texts; AMIA 2008 Symposium Proceedings, 2008.
• Wolfer, S.; Müller-Feldmeth, D.; Held, U.; Konieczny, L. and Auer, Petersen, K.; Investigating comprehensibility of intra-language translations: Introducing a reading corpus of popular science texts; Proceedings of the 7th EST Congress, 2013
• Wolfer, S.; Müller-Feldmeth, D.; Konieczny, L.; Held, U.; Maksymski, K.; Hansen-Schirra, S.; Hansen, S. and Auer, P.; PopSci: A reading corpus of popular science texts with rich multi-level annotations. A case study; Proceedings of the 17th European Conference on Eye Movements., 2013
• Clercq, O. D.; Hoste, V.; Desmet, B.; Oosten, P. V.; Cock, M. D. & Macken, L.; Using the crowd for readability prediction Natural Language Engineering, 2014, 1-33.
• François, T.; Gala, N.; Watrin, P.& Fairon, C. FLELex: a graded lexical resource for French foreignlearners; Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), 2014 (French)

Statistical and Machine learning approaches

• Language Modeling

• Si, L. and Callan, J.; A Statistical Model for Scientific Readability; Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM), ACM, 2001, 574-576.
• Collins-Thompson, K. and Callan, J.; A language modeling approach to predicting reading difficulty; Proceedings of HLT/NAACL 2004, 2004.
• Collins-Thompson, K. and Callan, J.; Predicting reading difficulty with statistical language models; Journal of the American Society for Information Science and Technology, 2005, 56, 1448-1462.
• Sato, S.; Matsuyoshi, S. and Kondoh, Y.; Automatic Assessment of Japanese Text Readability Based on a Textbook Corpus. LREC'08, 2008. (Japanese)

• Feature Engineering for Machine Learning

• Liu, X.; Croft, W. B.; Oh, P. and Hart, D.; Automatic recognition of reading levels from user queries; Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 2004, 548-549.
• Heilman, M.; Collins-Thompson, K.; Callan, J. and Eskenazi, M.; Combining Lexical and Grammatical Features to Improve Readability Measures for First and Second Language Texts; Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-07), 2007, 460-467.
• Vor der Brück, T.and Hartrumpf; A semantically oriented readability checker for German; Proceedings of the 3rd Languageand Technology Conference, Wydawnictwo Poznańskie, 2007, 270-274 (German).
• Heilman, M.; Collins-Thompson, K. and Eskenazi, M.; An Analysis of Statistical Models and Features for Reading Difficulty Prediction; Proceedings of the 3rd Workshop on Innovative Use of NLP for Building Educational Applications at ACL-08, 2008.
• Sheehan, K. M.; Kostin, I. and Futagi, Y.; When Do Standard Approaches for Measuring Vocabulary Difficulty, Syntactic Complexity and Referential Cohesion Yield Biased Estimates of Text Difficulty?; Proceedings of the 30th Annual Meeting of the Cognitive Science Society, 2008.
• Vor der Brück, T.; Hartrumpf, S and Helbig, H;. A Readability Checker with Supervised Learning using Deep Syntactic and Semantic Indicator; Informatica, 2008, 32, 42-435.(German)
• Vor der Brück, T.; Helbig, H and Leveling, J; The readability checker DeLite Fakulät für Mathematik und Informatik, FernUniversität in Hagen, .(German)
• Feng, L.; Elhadad, N. and Huenerfauth, M.; Cognitively Motivated Features for Readability Assessment; Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), Association for Computational Linguistics, 2009, 229-237.
• François, T. L; 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, 2009, 19-27. (French)
• Gottron, T. and Martin, L.; Estimating Web Site Readability Using Content Extraction; In Proceedings of the World Wide Web Conference (WWW) 2009.
• Kanungo, T. and Orr, D.; Predicting the readability of short web summaries; Proceedings of the Second ACM International Conference on Web Search and Data Mining, ACM, 2009, 202-211.
• Marujo, L.; Lopes, J.; Mamede, N.; Trancoso, I.; Pino, J.; Eskenazi, M.; Baptista, J. and Viana, C.; Porting REAP to European Portuguese; Proceedings of the SLaTE Workshop on Speech and Language Technology in Education, 2009. (Portuguese)
• Petersen, S. E. and Ostendorf, M.; A machine learning approach to reading level assessment; Computer Speech and Language, 2009, 23, 86-106.
• Al-Khalifa, H. S. and Al-Ajlan, A. A.; Automatic Readability measurements of the Arabic text: An exploratory study; The Arabian journal for science and engineering, 2010, 35. (Arabic)
• Aluisio, S.; Specia, L.; Gasperin, C. and Scarton, C.; Readability Assessment for Text Simplification; Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications, 2010, 1-9. (Portuguese)
• Feng, L.; Jansche, M.; Huenerfauth, M. and Elhadad, N.; A Comparison of Features for Automatic Readability Assessment; In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China., 2010.
• Kate, R. J.; Luo, X.; Patwardhan, S.; Franz, M.; Florian, R.; Mooney, R. J.; Roukos, S. and Welty, C.; Learning to Predict Readability using Diverse Linguistic Features; 23rd International Conference on Computational Linguistics (COLING 2010), 2010.
• Sheehan, K. M.; Kostin, I.; Futagi, Y. and Flor, M.; Generating Automated Text Complexity Classifications That Are Aligned With Targeted Text Complexity Standards; ETS, 2010.
• Tanaka-Ishii, K.; Tezuka, S. and Terada, H.; Sorting texts by readability; Comput. Linguist., MIT Press, 2010, 36, 203-227. (English, Japanese)
• Zhao, J. and Kan, M.-Y.; Domain-specific iterative readability computation; Proceedings of the 10th annual joint conference on Digital libraries, ACM, 2010, 205-214.
• Daowadung, P. and Chen, Y.-H.; Using word segmentation and SVM to assess readability of Thai text for primary school students; International Joint Conference on Computer Science and Software Engineering - JCSSE, 2011. (Thai language)
• Dell'Orletta, F.; Montemagni, S. and Venturi, G.; READ-IT: Assessing Readability of Italian Texts with a View to Text Simplification; Proceedings of the 2nd Workshop on Speech and Language Processing for Assistive Technologies, 2011, 73-83. (Italian)
• Francois, T. and Watrin, P.; On the Contribution of MWE-based Features to a Readability Formula for French as a Foreign Language; Proceedings of Recent Advances in Natural Language Processing, 2011, 441-447.(French)
• Kidwell, P.; Lebanon, G. and Collins-Thompson, K.; Statistical Estimation of Word Acquisition with Application to Readability Prediction; Journal of the American Statistical Association. 106(493):21-30, 2011.
• Qumsiyeh, R. and Ng, Y.-K.; ReadAid: A Robust and Fully-Automated Readability Assessment Tool; In Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, 2011
• Vajjala, S. and Meurers, D.; On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition; In Proceedings of the 7th Workshop on Innovative Use of NLP for Building Educational Applications, Association for Computational Linguistics, 2012, 163–173.
• Beinborn, L.; Zesch, T. and Gurevych, I.; Towards fine-grained readability measures for self-directed language learning; Proceedings of the SLTC 2012 workshop on NLP for CALL, 2012.
• Brooke, J.; Tsang, V.; Jacob, D.; Shein, F. and Hirst, G.; Building Readability Lexicons with Unannotated Corpora; Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, Association for Computational Linguistics, 2012, 33-39.
• Crossley, S. A.; Allen, D. and McNamara, D. S.; Text simplification and comprehensible input: A case for an intuitive approach; Language Teaching Research, 2012, 16.
• Daowadung, P. and Chen, Y.-H.; Stop Word in Readability Assessment of Thai Text; 12th IEEE International Conference on Advanced Learning Technologies, 2012. (Thai)
• Francois, T. and Fairon, C.; An “AI readability” formula for French as a foreign language; Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012. (French)
• Hancke, J.; Meurers, D. and Vajjala, S.; Readability Classification for German using lexical, syntactic, and morphological features; Proceedings of the 24th International Conference on Computational Linguistics (COLING), 2012, 1063-1080. (German)
• Islam, Z.; Mehler, A. and Rahman, R.; Text Readability Classification of Textbooks of a Low-Resource Language; 26th Pacific Asia Conference on Language,Information and Computation pages, 2012. (Bangla)
• Ma, Y.; Fosler-Lussier, E. and Lofthus, R.; Ranking-based readability assessment for early primary children's literature; Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, 2012, 548-552
• Ma, Y.; Singh, R.; Fosler-Lussier, E. and Lofthus, R.; Comparing human versus automatic feature extraction for fine-grained elementary readability assessment; Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, Association for Computational Linguistics, 2012, 58-64.
• Sjöholm, J.; Probability as readability:A new machine learning approach to readability assessment for written Swedish; Masters Thesis- Linköpings universitet, 2012. (Swedish)
• Flor, M.; Klebanov, B. B. and Sheehan, K. M.; Lexical Tightness and Text Complexity; Proceedings of the Second Workshop onNatural Language Processing for Improving Textual Accessibility, 2013.
• Sheehan, K. M.; Flor, M. and Napolitano, D.; A Two-Stage Approach for Generating Unbiased Estimates of Text Complexity; Proceedings of the Second Workshop on Natural Language Processing for Improving Textual Accessibility, 2013.
• Shen, W.; Williams, J.; Marius, T. and Salesky, E.; A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners; Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), 2013
• Johan Falkenjack, Katarina Heimann Mühlenbock, Arne Jönsson; Features indicating readability in Swedish text; NODALIDA 2013. (Swedish)
• Arifin, Z.; Halim, Z. A.; Sham, N. M. M. & Shukry, A. S. M.; The Readability of the Arabic Prose Text Middle-East Journal of Scientific Research, 2013, 17 (3), 338-343. (Arabic)
• Pilàn, I.; Volodina, E.& Johansson, R. Bradley, L. & Thouësny, S. (Eds.); Automatic Selection of Suitable Sentences for Language Learning Exercises 20 Years of EUROCALL: Learning from the Past, Looking to the Future. Proceedings of the 2013 EUROCALL Conference, 2013, 218-225. (Swedish)
• Todirascu, A.; François, T.; Gala, N.; Fairon, C.; Ligozat, A.-L. & Bernhard., D.; Coherence and Cohesion for the Assessment of TextReadability; Proceedings of the 10th International Workshop on Natural Language Processing and Cognitive Science, 2013. (French)
• Chen, Y.-T.; Chen, Y.-H. & Cheng, Y.-C; Assessing Chinese Readability using Term Frequency and Lexical Chain; Computational Linguistics and Chinese Language Processing; 2013, 18, 1-18. (Chinese)
• Sung, Y.-T.; Chen, J.-L.; Cha, J.-H.; Tseng, H.-C.; Chang, T.-H. & Chang, K.-E.; Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning. Behavior Research Methods, 2014,(online only version available now), 1-15. (Chinese)

• Discourse, Cohesion, Coherence, Coh-metrix etc.,

• McNamara, D. S. and the CSEP lab; Coh-Metrix: Automated cohesion and coherence scores to predict text readability and facilitate comprehension; CSEP lab, 2003.
• Williams, S.; Reiter, E. and Osman, L. M.; Experiments With Discourse-Level Choices and Readability; In Proceedings of the European Natural Language Generation Workshop (ENLG), 2003.
• Pitler, E. and Nenkova, A.; Revisiting readability: a unified framework for predicting text quality; Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2008, 186-195.
• Scarton, C. and Aluisio, S. M.; Coh-Metrix-Port: a readability assessment tool for texts in Brazilian Portuguese; Proceedings of the International Conference on Computational Processing of the Portuguese Language, 2010. (Brazilian Portuguese)
• Crossley, S. A.; Allen, D. B. and McNamara, D.; Text Readability and intuitive simplification: A comparison of readability formulas; Reading in a Foreign Language, 2011, 23, pp. 84-101.
• Jameel, S.; Lam, W. and Qian, X.; Ranking Text Documents Based on Conceptual Difficulty Using Term Embedding and Sequential Discourse Cohesion; Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, 2012, 1, 145-152.
• Sheehan, K.M.; Measuring Cohesion: An Approach That Accounts for Differences in the Degree of Integration Challenge Presented by Different Types of Sentences; Educational Measurement: Issues and Practice, 2013, (online first)

User studies, eye tracking etc.,

• Vauras, M.; Hyönä, J. and Niemi, P.; Comprehension of coherent and incoherent texts: Evidence from eye movement patterns and recall performance; Journal of Reading Research, 1992, 15, 39-54.
• Rayner, K.; Eye movements in reading and information processing: 20 years of research; Psychological Bulletin, 1998, 124, 372-422.
• van Oosten, P. and Hoste, V.; Readability annotation: replacing the expert by the crowd; Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications, Association for Computational Linguistics, 2011, 120-129. (Dutch)
• Siddharthan, A. and Katsos, N.; Offline Sentence Processing Measures for testing Readability with Users; Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, Association for Computational Linguistics, 2012, 17-24.
• Hara, T.; Chen, C.; Kano, Y. and Aizawa, A.; Modeling Comma Placement in Chinese Text for Better Readability using Linguistic Features and Gaze Information; Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), 2013. (Chinese)
• Nishikawa, H.; Makino, T. and Matsuo, Y.; A Pilot Study of Readability Prediction with Reading Time;Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations, 2013.
• Medero, J. and Ostendorf, M.; Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty; Proceedings of NAACL-HLT 2013, Assoc. for Computational Linguistics, 2013.
• Lenzner, T.; Are Readability Formulas Valid Tools for Assessing Survey Question Difficulty?; Sociological Methods and Research, 2013.

Language Acquisition, Psycholinguistic and Cognitive perspectives

• Just, M. and Carpenter, P.; A theory of reading: From eye fixations to comprehension; Psychological Review, 1980, 87, 329-35.
• Lu, X.; A Corpus-Based Evaluation of Syntactic Complexity Measures as Indices of College-Level ESL Writers' Language Development; TESOL Quarterly, Teachers of English to Speakers of Other Languages, Inc. (TESOL), 2011, 45, 36-62.
• van Schijndel, M.; Nguyen, L. and Schuler, W.; An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing; Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 2013.
• van Schijndel, M. and Schuler, W.; An Analysis of Frequency and Memory-Based Processing Costs; Proceedings of NAACL-HLT, 2013, 95-105.
• van der Sluis, F.; van Dijk, E. M. A. G.; de Jong, F. M. G.; van den Broek, E. L. & Glassey, R. J.; When Complexity Becomes Interesting. Journal of the Association for Information Science and Technology, 2014.
• Solorio, T.; Survey on Emerging Research on the Use of Natural Language Processing in Clinical Language Assessment of Children Language and Linguistics Compass, 2013. 7, 633-646.

Tools

Research focused on applications

• Analysing Textbooks
• Agnihotri, R. and Khanna, A.; Evaluating the readability of school textbooks: An Indian study; Journal of Reading, 1991, 35, 282-288.
• Flory, S. M.; Jr., T. J. P. and Tassin, M. F.; Measuring readability: A comparison of accounting textbooks; Journal of Accounting Education, 1992, 10, 151-161.
• Cepni, S.; Gökdere, M. and ücük, M.; Adaptation of the readability formulas for Turkish science textbooks; Energy Education Science and Technology, 2002, 49-58.
• Nandhini, K. and Balasundaram, S.R.; Improving readability through extractive summarization for learners with reading difficulties; Egyptian Informatics Journal, 2013.
• Begeny, J. C. & Greene, D. J.; Can Readability Formulas be successfully used to gauge the difficulty of reading materials?; Psychology in the Schools, 2013, 1520-6807.
• Information Retrieval, Personalized search etc.,
• Mothey, J. and Tanguy, L.; Linguistic Features to predict Query Difficulty; ACM SIGIR Workshop on predicting query difficulty-methods and applications; 2005
• Lorna Kane, J. C. and Dunnion, J.; Readability Applied to Information Retrieval; Springer, 2006, 3936, 523-526.
• Quarteroni, S. and Manandhar, S.; Incorporating User Models in Question Answering to Improve Readability; Proceedings of KRAQ'06, 2006.
• Yan, X.; Song, D. and Li, X.; Concept-based document readability in domain specific information retrieval; CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management, ACM, 2006, 540-549.
• Miyazaki, Y. and Norizuki, K.; Developing a Computerized Readability Estimation Program with a Web-searching Function to Match Text Difficulty with Individual Learners’ Reading Ability; Proceedings of the WorldCALL Conference, 2008.
• Miltsakaki, E.; Matching readers' preferences and reading skills with appropriate web texts; Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations Session, Association for Computational Linguistics, 2009, 49-52.
• Nakatani, M.; Jatowt, A. and Tanaka, K.; Adaptive Ranking of Search Results by Considering User's Comprehension; Proceedings of the 4th International Conference on Ubiquitous Information Management and Communication (ICUIMC 2010), ACM Press, Suwon, Korea, 2010, 182-192.
• Newbold, N.; McLaughlin, H. and Gillam, L. Cunningham, H.; Hanbury, A. and Rüger, S. (Eds.) Rank by Readability: Document Weighting for Information Retrieval Advances in Multidisciplinary Retrieval, Springer Berlin / Heidelberg, 2010, 6107, 20-30
• Ott, N. and Meurers, D.; Information Retrieval for Education: Making Search Engines Language Aware; Themes in Science and Technology Education. Special issue on computer-aided language analysis, teaching and learning: Approaches, perspectives and applications, 2010, 3, 9-30.
• Wan, X.; Li, H. and Xiao, J.; EUSUM: extracting easy-to-understand english summaries for non-native readers; Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, ACM, 2010, 491-498.
• Collins-Thompson, K.; Improving information retrieval with reading level prediction; SIGIR 2011 Workshop on Enriching Information Retrieval, 2011.
• Vlachos, M. and Lappas, T.; Ranking German texts by Comprehensibility for Foreign Document Retrieval; In Proceedings of SIGIR workshop on enriching Information Retrieval, 2011. (German)
• Martin, L. and Gottron, T.; Readability and the Web; Future Internet, 2012, 4, 238-252.
• Pera, M. S. and Ng, Y.-K.; BReK12: A Book Recommender for K-12 Users; In Proceedings of SIGIR, ACM, 2012
• Tan, C.; Gabrilovich, E. and Pang, B.; To Each His Own: Personalized Content Selection based on Text Comprehensibility; In Proceedings of WSDM, 2012
• Vajjala, S. and Meurers, D.; On The Applicability of Readability Models to Web Texts; Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations, 2013.
• Other applications
• Wong, I.; Readability of patient information leaflets on antiepileptic drugs in the UK; Seizure, 1999, 8, 35-37.
• Leonga, E. K.; Ewingb, M. T. and Pittb, L. F.; E-comprehension: Evaluating B2B websites using readability formulae; Industrial Marketing Management, 2002, 31, 125-131.
• Lau, T. P. and King, I.; Bilingual Web Page and Site Readability Assessment; In Proceedings of the World Wide Web Conference (WWW), 2006.
• Aluisio, S.; Specia, L.; Gasperin, C. and Scarton, C.; Readability Assessment for Text Simplification, 2007. (Portuguese)
• Woodmansey, K.; Readability of Educational Materials for Endodontic Patients; Journal of Endodontics, 2010, 36, 1703-1706.
• Ellimoottil, C.; Polcari, A.; Kadlec, A. and Gupta, G.; Readability of Websites Containing Information About Prostate Cancer Treatment Options; 2012, 188, 2171-2176.
• Howes, L. M.; Kirkbride, K. P.; Kelty, S. F.; Julian, R. and Kemp, N.; Forensic scientists’ conclusions: How readable are they for non-scientists report-users?; Forensic Science International, 2013, 231, 102-112.
• Sara Stymne, Jörg Tiedemann, Christian Hardmeier, Joakim Nivre; Statistical Machine Translation with Readability Constraints; NODALIDA 2013.
• Pringle M.B.; Natesh, B.G.; and Konieczny, K.M.; Patient information leaflet on mastoid surgery risks: assessment of readability and patient understanding; The Journal of Laryngology and Ontology, 2013.
• Suzanne S Dunne; Niamh M Cummins; Ailish Hannigan; Bill Shannon; Colum Dunne and Walter Cullen; Generic medicines: an evaluation of the accuracy and accessibilityof information available on the internet; BMC Medical Informatics and Decision Making; 2013.
• Chung, J.-W.; Min, H.-J.; Kim, J. & Park, J. C.; Enhancing readability of web documents by text augmentation for deaf people; Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, 2013.
• Febres, G. & Jaffe, K. Quantifying literature quality using complexity criteria 2014. (Arxiv)
• Dagar, A.; Punia, V.; Agarwal, N.; He, W.; and Hillen, M.; Comparison of American Academy of Neurology's Online Patient Education Materials to Other Common Internet Sources. Neurology April 8, 2014 vol. 82 no. 10 Supplement P1.332

Discussions, Corpus/User studies, Evaluation etc.,

• Dufty, D. F.; Graesser, A. C.; Louwerse, M. M. and McNamara, D. S.; Assigning grade levels to textbooks: Is it just readability?; Proceedings of the 28th Annual Meetings of the Cognitive Science Society, 2006, 1251-1256.
• Ulusoy, M.; Readability approaches: Implications for Turkey; University of Illinois at Urbana-Champaign, 2006.
• Huenerfauth, M.; Feng, L. and Elhadad, N.; Comparing evaluation techniques for text readability software for adults with intellectual disabilities. Proceedings of the 11th international ACM SIGACCESS conference on Comput ers and accessibility, ACM, 2009, 3-10.
• van Oosten, P.; Tanghe, D. and Hoste, V.; Towards an Improved Methodology for Automated Readability Prediction; LREC'10, 2010.
• Landauer, T. K.; Pearson's Text Complexity Measure; Pearson, 2011.
• Van Oosten, P.; Hoste, V. and Tanghe, D.; A posteriori agreement as a quality measure for readability prediction systems; Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II, Springer-Verlag, 2011, 424-435.
• Benjamin, R. G.; Reconstructing Readability: Recent Developments and Recommendations in the Analysis of Text Difficulty; Educational Psychology Review, 2012, 24, 63-88
• François, T. and Miltsakaki, E.; Do NLP and machine learning improve traditional readability formulas?; Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, Association for Computational Linguistics, 2012, 49-57.
• Nelson, J.; Perfetti, C.; Liben, D. and Liben, M.; Measures of Text Difficulty: Testing their Predictive Value for Grade Levels and Student Performance; The Council of Chief State School Officers, 2012.
• Tonelli, S.; Tran Manh, K. and Pianta, E.; Making Readability Indices Readable; Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations, Association for Computational Linguistics, 2012, 40-48.
• Stajner, S.; Evans, R.; Orasan, C. and Mitkov, R.; What Can Readability Measures Really Tell Us About Text Complexity?; In Proceedings of the First Workshop on Natural Language Processing for Improving Textual Accessibility, European Language Resources Association (ELRA), 2012
• Wray, D. and Janan, D.; Readability revisited? The implications of text complexity; The Curriculum Journal, 2013.
• Jones, D.; Wolf, F.; Gibson, E.; Williams, E.; Fedorenko, E.; Reynolds, D. and Zissman, M. Measuring the readability of automatic speech-to-text transcripts 8th European Conference on Speech Communication and Technology, EUROSPEECH 2003 - INTERSPEECH 2003, ISCA, 2003, 1585-1588.
• Phani, S.; Lahiri, S. & Biswas, A.; Inter-rater Agreement Study on Readability Assessment in Bengali, International Conference On Natural Language Processing And Cognitive Computing (ICONACC)-2014, 2014.
• Fisher, D. & Frey, N.; Addressing CCSS Anchor Standard 10: Text Complexity; Language Arts, 2014, 91(4), 236-250.

Various Masters and PhD Theses

• Bennöhr, J.; A Web-based Personalised Textfinder for Language Learners; Masters Thesis, School of Informatics, University of Edinburgh, 2005.
• Larsson, P. and Megyesi, B.; Classification into Readability Levels Implementation and Evaluation; Uppsala Universitet, Department of Linguistics and Philology, Language Technology Programme, 2006 Masters Thesis. (Swedish)
• Lau, T. P.; Chinese Readability Analysis and Its Applications on the Internet; CUHK, Hongkong, 2006 - Masters Thesis. (Chinese)
• Petersen, S. E.; Natural Language Processing Tools for Reading Level Assessment and Text Simplification for Bilingual Education; University of Washington PhD Thesis, 2007.
• Ott, N.; Information Retrieval for Language Learning: An Exploration of Text Difficulty Measures; Masters Thesis - Universität Tübingen, Seminar für Sprachwissenschaft, 2009.
• dos Santos Marujo, L. C.; REAP.PT (Reap-Portuguese); Masters Thesis- Universidade Tecnica de Lisboa, 2009. (Portuguese)
• Tanguy, L. and Tulechki, N.; Sentence Complexity in French: a Corpus-Based approach; Intelligent Information Systems, 2009, 1-14. (French)
• Lijun Feng; Automatic Readability Assessment; PhD Thesis - City University of New York (CUNY), 2010.
• Heimann Möhlenbock, K.; I see what you mean:Assessing readability for specific target groups; University of Gothenburg, 2013. (Swedish)

Workshops focused on the topic or have papers relevant to this topic

1. The First Workshop on Predicting and Improving Text Readability for Target Reader Populations, 2012 (PITR 2012)
2. The Second Workshop on Predicting and Improving Text Readability for Target Reader Populations, 2013 (PITR 2013)
3. Natural Language Processing for Improving Textual Accessibility (NLP4ITA) 2012
4. NLP4ITA 2013
5. Workshop on Speech and Language Processing Tools in Education (SLP-TED 2012)
6. the SLaTE workshop series (The ISCA Special Interest Group in Speech and Language Technology in Education)
7. The BEA workshop series (Workshop on Innovative Use of NLP for Building Educational Applications)