ISCL Hauptseminar (Winter semester 2019)

Computational Linguistic Analysis of Linguistic Complexity for Readability and Proficiency Assessment


Notions of complexity surface in a number of different contexts: In theoretical linguistics, syntactic structures are analyzed in terms of their complexity and constraints such as the complex-NP constraint are formulated on this basis. In cognitive psychology, the complexity involved in cognitively processing language input in human sentence processing is studied. In second language acquisition research, the analysis of complexity (together with accuracy and fluency) is used to gain insights into the process and product of acquisition. In language testing and learner corpus research, the linguistic complexity of learner language is related to proficiency levels. For readability research, the linguistic complexity is used to determine who a given text is readable for.

In this seminar, we will discuss the empirical and conceptual nature of these notions of complexity and explore where the formalization and automatic analysis offered by computational linguistics can lead to applications such as automatic readability measures, proficiency classification, and search engines supporting the filtering of results by complexity for particular target audiences.


Course meets: 4 SWS

Credit Points:

Syllabus (this file):

Moodle page:

Please enroll in this course by logging into this moodle course.

Nature of course and our expectations: This is a research-oriented, hands-on Hauptseminar, in which we jointly explore the topic and gain practical experience in implementing analyses. Substantial programming experience (at least at the level of the second Data Structures and Algorithms course) is required; permission may be granted for teams of two people combining complementary expertise. Everyone is expected to

  1. successfully complete the regular exercises and small projects assigned during the semester and present the results to the seminar,
  2. regularly and actively participate in class, read the assigned papers and post a meaningful question on Moodle to the “Discussion Forum” on each reading at the latest on the day before the topic is discussed in class.
  3. explore and present a topic (individually or as part of a group)
  4. if you pursue the 9 CP option, work out a project term paper

Academic conduct and misconduct: Research is driven by discussion and free exchange of ideas, motivations, and perspectives. So you are encouraged to work in groups, discuss, and exchange ideas. At the same time, the foundation of the free exchange of ideas is that everyone is open about where they obtained which information. Concretely, this means you are expected to always make explicit when you’ve worked on something as a team – and keep in mind that being part of a team always means sharing the work.

For text you write, you always have to provide explicit references for any ideas or passages you reuse from somewhere else. Note that this includes text “found” on the web, where you should cite the url of the web site in case no more official publication is available.

Class etiquette: Please do not read or work on materials for other classes in our seminar. All portable electronic devices such as cell phones and laptops should be switched off for the entire length of the flight, oops, class.

Sketch of assignments

  1. Traditional readability: Flesch-Kincaid formula
  2. Lexical complexity: lexical richness, frequency
  3. Syntactic complexity
  4. Psycholinguistic perspective (Ted Gibson’s DLT)
  5. Discourse: aspects of cohesion (connectives, overlap, coreference, …)

Data sources to be used include:


  1. 23./25.10. Detmar: Introduction
  2. 30.10. Zarah: Readability Formulas (no class on 1.11. holiday)
  3. 6./8.11. Detmar: Introduction (cont.)
  4. 13.11. Xiaobin: Aggregating lexical-level complexity information to predict the text-level complexity (Chen & Meurers 2017)
  5. 15.11. Xiaobin: Linking text readability and learner proficiency using linguistic complexity feature vector distance (Chen & Meurers 2019)
  6. 20.11 Tanja Heck: Lexical complexiy (Laufer & Nation 1995)
  7. 22.11. Eva Huber: Morphological complexity (Paquot 2019)
  8. 27.11. Xiaobin Chen, Zarah Weiss: Longitudinal Development of Complexity and Accuracy
  9. 29.11. Haemanth Santhi-Ponnusamy: Syntactic complexity in college-level English writing and L1 differences (Lu & Ai 2015)
  10. 4.12. Elizabeth Bear: The Development of Second Language Writing Complexity in Groups and Individuals: A Longitudinal Learner Corpus Study (Vyatkina 2012)
  11. 6.12. Zarah Weiss: Analyzing linguistic complexity and accuracy in academic language development (Weiss & Meurers 2019a)
  12. 11.12 Hebah Ahmed: Automatic Measurement of Syntactic Complexity Using the Revised Developmental Level Scale (Lu 2009Voss 2005)
  13. 13.12. Daniela Rossman: IPSyn (Sagae et al. 2005Lubetich & Sagae 2014)
  14. 18.12. Jana Murasová: Comparing child L2 development with adult L2 development (Unsworth 2008)
  15. 20.12. Sarah Neuhaus: Readability assessment for aphasia: (Aleligay et al. 2008Abou-Diab et al. 2019) (and Discussion of Christmas break project)
  16. 8.1 Zarah Weiss: Dependency Locality Theory (Gibson 2000Shain et al. 2016)
  17. 10.1 Mareile Winkler: Propositional Idea Density (Brown et al. 2008)
  18. 15.1. Masoumeh Moradipour-tari: Discourse/Cohesion (Graesser et al. 2004)
  19. 17.1 Xiaobin Chen: CTAP (?) and its UIMA architecture
  20. 22.1. Nelly Sagirov: Testing target text fluency
  21. 24.1. Denise Loefflad: Redability analysis for French as a foreign language (François & Fairon 2012)
  22. 29.1. Mohamed Ouji: Evaluation (Huenerfauth et al. 2009van Oosten et al. 2010Van Oosten et al. 2011)
  23. 31.1
  24. 5.2.

Topics (first sketch: this will develop as the semester proceeds)


   Abou-Diab, S. N., D. C. Moser & S. R. Atcherson (2019). Evaluation of the readability, validity, and user-friendliness of written web-based patient education materials for aphasia. Aphasiology 33(2), 187–199.

   Ai, H. & X. Lu (2013). A corpus-based comparison of syntactic complexity in NNS and NS university students’ writing. In A. Díaz-Negrillo, N. Ballier & P. Thompson (eds.), Automatic Treatment and Analysis of Learner Corpus Data, John Benjamins, pp. 249–264.

   Aleligay, A., L. E. Worrall & T. A. Rose (2008). Readability of written health information provided to people with aphasia. Aphasiology 22(4), 383–407.

   Alexopoulou, T., M. Michel, A. Murakami & D. Meurers (2017). Task Effects on Linguistic Complexity and Accuracy: A Large-Scale Learner Corpus Analysis Employing Natural Language Processing Techniques. Language Learning 67, 181–209. URL

   Boston, M. F., J. T. Hale, U. Patil, R. Kliegl & S. Vasishth (2008). Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus. Journal of Eye Movement Research 2(1), 1–12. URL

   Boston, M. F., J. T. Hale, S. Vasishth & R. Kliegl (2011). Parallel processing and sentence comprehension difficulty. Language and Cognitive Processes 26(3), 301–349.

   Brezina, V. & G. Pallotti (2019). Morphological complexity in written L2 texts. Second language research 35(1), 99–119.

   Brown, C., T. Snodgrass, S. J. Kemper, R. Herman & M. A. Covington (2008). Automatic measurement of propositional idea density from part-of-speech tagging. Behavior Research Methods 40(2), 540–545.

   Chen, X. & D. Meurers (2017). Word frequency and readability: Predicting the text-level readability with a lexical-level attribute. Journal of Research in Reading 41(3), 486–510.

   Chen, X. & D. Meurers (2019). Linking text readability and learner proficiency using linguistic complexity feature vector distance. Computer-Assisted Language Learning

   Cheung, H. & S. Kemper (1992). Competing complexity metrics and adults’ production of complex sentences. Applied Psycholinguistics 13(01), 53–76. URL

   Covington, M. A., C. He, C. Brown, L. Naçi & J. Brown (2006). How complex is that sentence? A proposed revision of the Rosenberg and Abbeduto D-Level Scale. Computer Analysis of Speech for Psychological Research (CASPR) Research Report 2006-01, The University of Georgia, Artificial Intelligence Center, Athens, GA. URL

   Crossley, S., D. F. Dufty, P. M. McCarthy & D. S. Mcnamara (2007). Toward a New Readability: A Mixed Model Approach. In Proceedings of the 29th annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society, pp. 197–202.

   Crossley, S. A., J. Greenfield & D. S. McNamara (2008). 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, pp. 475–493.

   Crossley, S. A. & D. S. McNamara (2014). Does writing development equal writing quality? A computational investigation of syntactic complexity in L2 learners. Journal of Second Language Writing 26, 66–79.

   Dell’Orletta, F., S. Montemagni & G. Venturi (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.

   Demberg, V. & F. Keller (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition 109(2), 193 – 210.

   Demberg, V. & A. Sayeed (2011). Linguistic cognitive load: implications for automotive UIs. In Adjunct Proceedings of AutomotiveUI’11.

   DuBay, W. H. (2004). The Principles of Readability. Costa Mesa, California: Impact Information. URL

   DuBay, W. H. (2006). The Classic Readability Studies. Costa Mesa, California: Impact Information.

   François, T. & C. Fairon (2012). An “AI readability” formula for French as a foreign language. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.

   François, T. & E. Miltsakaki (2012). Do NLP and machine learning improve traditional readability formulas? In Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations. Association for Computational Linguistics, pp. 49–57.

   Georgatou, S. (2016). Approaching readability features in Greek school books. Master thesis in computational linguistics, Department of Linguistics, University of Tübingen.

   Gibson, E. (2000). The dependency locality theory: A distance-based theory of linguistic complexity. In A. Marantz, Y. Miyashita & W. O’Neil (eds.), Image, language, brain: papers from the First Mind Articulation Project Symposium, MIT, pp. 95–126.

   Graesser, A. C., D. S. McNamara, M. M. Louweerse & Z. Cai (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments and Computers 36, 193–202. URL

   Hancke, J. (2013). Automatic Prediction of CEFR Proficiency Levels Based on Linguistic Features of Learner Language. Master’s thesis, International Studies in Computational Linguistics. Seminar für Sprachwissenschaft, Universität Tübingen.

   Hancke, J. & D. Meurers (2013). Exploring CEFR classification for German based on rich linguistic modeling. In Learner Corpus Research 2013, Book of Abstracts. Bergen, Norway. URL

   Hancke, J., S. Vajjala & D. Meurers (2012). Readability Classification for German using lexical, syntactic, and morphological features. In Proceedings of the 24th International Conference on Computational Linguistics (COLING). Mumbay, India, pp. 1063–1080.

   Housen, A. & F. Kuiken (2009). Complexity, Accuracy and Fluency in Second Language Acquisition. Applied Linguistics 30(4), 461–473.

   Huenerfauth, M., L. Feng & N. Elhadad (2009). Comparing evaluation techniques for text readability software for adults with intellectual disabilities. In Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility. New York, NY, USA: ACM, Assets ’09, pp. 3–10.

   Kyle, K. (2016). Measuring Syntactic Development in L2 Writing: Fine Grained Indices of Syntactic Complexity and Usage-Based Indices of Syntactic Sophistication. Ph.D. thesis, Georgia State University. URL

   Kyle, K. & S. A. Crossley (2015). Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application. TESOL Quarterly 49(4), 757–786.

   Laufer, B. & P. Nation (1995). Vocabulary Size and Use: Lexical Richness in L2 Written Production. Applied Linguistics 16(3), 307–322. URL

   Levy, R. (2008). Expectation-based syntactic comprehension. Cognition 106 (3), 1126–1177.

   Levy, R. & E. Gibson (2013). Surprisal, the PDC, and the primary locus of processing difficulty in relative clauses. Frontiers in Language Sciences 4:229, 1–3.

   Lu, X. (2009). Automatic measurement of syntactic complexity in child language acquisition. International Journal of Corpus Linguistics 14(1), 3–28.

   Lu, X. (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics 15(4), 474–496.

   Lu, X. (2011). A Corpus-Based Evaluation of Syntactic Complexity Measures as Indices of College-Level ESL Writers’ Language Development. TESOL Quarterly 45(1), 36–62.

   Lu, X. (2012). The Relationship of Lexical Richness to the Quality of ESL Learners’ Oral Narratives. The Modern Languages Journal pp. 190–208.

   Lu, X. & H. Ai (2015). Syntactic complexity in college-level English writing: Differences among writers with diverse L1 backgrounds. Journal of Second Language Writing 29, 16–27.

   Lubetich, S. & K. Sagae (2014). Data-driven Measurement of Child Language Development with Simple Syntactic Templates. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Dublin, Ireland: Dublin City University and Association for Computational Linguistics, pp. 2151–2160. URL

   Malvern, D. D., R. B. J., C. N. & D. P. (2004). Lexical diversity and language development: Quantification and assessment. Palgrave Macmillan.

   McCarthy, P. & S. Jarvis (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods 42(2), 381–392.

   McNamara, D. S., M. M. Louwerse & A. C. Graesser (2002). Coh-Metrix: Automated Cohesion and Coherence Scores to Predict Text Readability and Facilitate Comprehension. Proposal of Project funded by the Office of Educational Research and Improvement, Reading Program. URL

   Nikolova, L. (2015). Readability Classification for Bulgarian. Master thesis in computational linguistics, Department of Linguistics, University of Tübingen.

   Ortega, L. (2003). Syntactic complexity measures and their relationship to L2 proficiency: A research synthesis of college-level L2 writing. Applied Linguistics 24(4), 492–518.

   Paquot, M. (2019). The phraseological dimension in interlanguage complexity research. Second Language Research 35(1), 121–145.

   Pilán, I., S. Vajjala & E. Volodina (2015). A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity. In Proceedings of CICLING 2015- Research in Computing Science Journal Issue (to appear).

   Read, J. & P. Nation (2004). Measurement of formulaic sequences. Formulaic sequences: Acquisition, processing and use pp. 23–35.

   Reynolds, R. (2016). Russian natural language processing for computer-assisted language learning: capturing the benefits of deep morphological analysis in real-life applications. Ph.D. thesis, UiT - The Arctic University of Norway. URL

   Sagae, K., A. Lavie & B. MacWhinney (2005). Automatic measurement of syntactic development in child language. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL-05). Ann Arbor, MI.

   Shain, C., M. van Schijndel, R. Futrell, E. Gibson & W. Schuler (2016). Memory access during incremental sentence processing causes reading time latency. In Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC). Osaka, pp. 49–58. URL

   Skehan, P. (1989). Individual Differences in Second Language Learning. Edward Arnold.

   Unsworth, S. (2008). Comparing child L2 development with adult L2 development. In Current trends in child second language acquisition: A generative perspective, John Benjamins Publishing, pp. 301–346.

   Van Oosten, P., V. Hoste & D. Tanghe (2011). A posteriori agreement as a quality measure for readability prediction systems. In Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II. Berlin, Heidelberg: Springer-Verlag, CICLing’11, pp. 424–435. URL

   van Oosten, P., D. Tanghe & V. Hoste (2010). Towards an Improved Methodology for Automated Readability Prediction. In LREC’10. pp. –1–1. URL

   van Schijndel, M. & W. Schuler (2016). Addressing surprisal deficiencies in reading time models. In Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC) at COLING. Osaka.

   Vor der Brück, T., S. Hartrumpf & H. Helbig (2008a). A Readability Checker with Supervised Learning using Deep Syntactic and Semantic Indicators. Informatica 32(4), 429–435.

   Vor der Brück, T., H. Helbig & J. Leveling (2008b). The readability checker DeLite. Tech. Rep. Technical Report 345-5/2008, Fakultät für Mathematik und Informatik, FernUniversität in Hagen.

   Voss, M. J. (2005). Determining Syntactic Complexity Using Very Shallow Parsing. Research Report 2005-01, Computer Analysis of Speech for Psychological Research (CASPR), Institute for Artificial Intelligence, The University of Georgia. URL Published verison of MSc thesis.

   Vyatkina, N. (2012). The Development of Second Language Writing Complexity in Groups and Individuals: A Longitudinal Learner Corpus Study. The Modern Language Journal 96(4), 576–598. URL

   Weiss, Z. (2017). Using Measures of Linguistic Complexity to Assess German L2 Proficiency in Learner Corpora under Consideration of Task-Effects. Master’s thesis, University of Tübingen, Germany. URL

   Weiss, Z. & D. Meurers (2018). Modeling the Readability of German Targeting Adults and Children: An Empirically Broad Analysis and its Cross-Corpus Validation. In Proceedings of the 27th International Conference on Computational Linguistics (COLING). Santa Fe, New Mexico, USA.

   Weiss, Z. & D. Meurers (2019a). Analyzing Linguistic Complexity and Accuracy in Academic Language Development of German across Elementary and Secondary School. In Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications (BEA). Florence, Italy: Association for Computational Linguistics.

   Weiss, Z. & D. Meurers (2019b). Broad Linguistic Modeling is Beneficial for German L2 Proficiency Assessment. In A. Abel, A. Glaznieks, V. Lyding & L. Nicolas (eds.), Widening the Scope of Learner Corpus Research. Selected Papers from the Fourth Learner Corpus Research Conference. Louvain-La-Neuve: Presses Universitaires de Louvain.

   Weiss, Z., A. Riemenschneider, P. Schröter & D. Meurers (2019). Computationally Modeling the Impact of Task-Appropriate Language Complexity and Accuracy on Human Grading of German Essays. In Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications (BEA). Florence, Italy.

   Wolfe-Quintero, K., S. Inagaki & H.-Y. Kim (1998). Second Language Development in Writing: Measures of Fluency, Accuracy & Complexity. Honolulu: Second Language Teaching & Curriculum Center, University of Hawaii at Manoa. URL