TextInContext: On the Way to a Framework for Measuring the Context-Sensitive Complexity of Educationally Relevant Texts—A Combined Cognitive and Computational Linguistic Approach

  • Alexander MehlerEmail author
  • Visvanathan Ramesh


We develop a framework for modeling the context sensitivity of text interpretation. As a point of reference, we focus on the complexity of educational texts. To open up a broader basis for representing phenomena of context sensitivity, we integrate a learning theory (i.e., the Cognitive Load Theory) with a theory of discourse comprehension (i.e., the Construction Integration Model) and a theory of cognitive semantics (i.e., the theory of Conceptual Spaces). The aim is to construct measures that view text complexity as a relational attribute by analogy to the relational concept of meaning in situation semantics. To this end, we reconstruct the situation semantic notion of relational meaning from the perspective of a computationally informed cognitive semantics. The aim is to prepare the development of measurements for predicting learning outcomes in the form of positive or negative learning. This prediction ideally depends on the underlying learning material, the learner’s situational context, and knowledge retrieved from his or her long-term memory, which he or she uses to arrive at coherent mental representations of the underlying texts. Finally, our model refers to machine learning as a tool for modeling such memory content. In this way, the chapter integrates approaches from different disciplines (linguistic semantics, computational linguistics, cognitive science, and data science).


Educational text mining Text complexity Context sensitivity Cognitive load theory Construction integration model Conceptual spaces Multimodality 


  1. Andersen, P. B. (2002). Dynamic semiotics. Semiotica, 1(4), 161–210.Google Scholar
  2. Anderson, R. C., & Davison, A. (1988). Conceptual and empirical bases of readability formulas. In R. C. Anderson & A. Davison (Eds.), Linguistic complexity and text comprehension (pp. 23–54). Hillsdale, MI: Erlbaum.Google Scholar
  3. Auer, P. (1992). Introduction: John Gumperz’ approach to contextualization. In P. Auer & A. Di Luzio (Eds.), The contextualization of language (pp. 1–37). Amsterdam: Benjamins.CrossRefGoogle Scholar
  4. Auer, P. (1996). From context to contextualization. Links & Letters, 3, 11–28.Google Scholar
  5. Barwise, J., & Perry, J. (1983). Situations and attitudes. Cambridge: MIT Press.Google Scholar
  6. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, 107–117.CrossRefGoogle Scholar
  7. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1), 87–110.CrossRefGoogle Scholar
  8. Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of Management Review, 13(1), 40–52.CrossRefGoogle Scholar
  9. Dagan, I., Roth, D., Sammons, M., & Zanzotto, F. M. (2013). Recognizing textual entailment: Models and applications. Synthesis lectures on human language technologies, San Rafael, CA: Morgan and Claypool, 6(4): 1–220.Google Scholar
  10. Eikmeyer, H.-J. (1985). Prozedurale Semantik. In B. Rieger (Ed.), Dynamik in der Bedeutungskonstitution, Papiere zur Textlinguistik (Vol. Bd. 46, pp. 31–45). Hamburg: Buske.Google Scholar
  11. Gärdenfors, P. (2000). Conceptual Spaces. Cambridge: MIT Press.CrossRefGoogle Scholar
  12. Gärdenfors, P. (2014). The geometry of meaning: Semantics based on conceptual spaces. Cambridge: MIT Press.CrossRefGoogle Scholar
  13. Goodwin, C., & Duranti, A. (1992). Rethinking context: An introduction. In A. Duranti & C. Goodwin (Eds.), Rethinking context: Language as an interactive phenomenon (pp. 1–42). Cambridge: Cambridge University Press.Google Scholar
  14. Greiffenhagen, M., Comaniciu, D., Niemann, H., & Ramesh, V. (2001). Design, analysis, and engineering of video monitoring systems: An approach and a case study. Proceedings of the IEEE, 89(10), 1498–1517.CrossRefGoogle Scholar
  15. Gumperz, J. J. (1992). Contextualization and understanding. In A. Duranti & C. Goodwin (Eds.), Rethinking context: Language as an interactive phenomenon (pp. 1–42). Cambridge: Cambridge University Press.Google Scholar
  16. Hunston, S., & Francis, G. (2000). Pattern grammar. a Corpus-driven approach to the lexical grammar of English. Amsterdam: John Benjamins.CrossRefGoogle Scholar
  17. Islam, Z., & Mehler, A. (2013). Automatic readability classification of crowd-sourced data based on linguistic and information-theoretic features. In 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2013).Google Scholar
  18. John, M. F. S., & McClelland, J. L. (1992). Parallel constraint satisfaction as a comprehension mechanism. In R. G. Reilly & N. E. Sharkey (Eds.), Connectionist approaches to natural language processing (pp. 97–136). Hove: Erlbaum.Google Scholar
  19. Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95(2), 163–182.CrossRefGoogle Scholar
  20. Kintsch, W. (1998). Comprehension. A paradigm for cognition. Cambridge: Cambridge University Press.Google Scholar
  21. Kintsch, W. (2001). Predication. Cognitive Science, 25, 173–202.CrossRefGoogle Scholar
  22. Kintsch, W. (2008). How the mind computes the meaning of metaphor: A simulation based on LSA. In R. W. Gibbs (Ed.), The Cambridge handbook of metaphor and thought (pp. 129–142). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  23. Kirschner, F., Paas, F., & Kirschner, P. (2011a). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25(4), 615–624. CrossRefGoogle Scholar
  24. Kirschner, P. A., Ayres, P., & Chandler, P. (2011b). Contemporary cognitive load theory research: The good, the bad and the ugly. Computers in Human Behavior, 27(1), 99–105.CrossRefGoogle Scholar
  25. Koch, P., & Oesterreicher, W. (1994). Schriftlichkeit und Sprache. In H. Günther & O. Ludwig (Eds.), Schrift und Schriftlichkeit: Ein interdisziplinäres Handbuch internationaler Forschung (Vol. 1, pp. 587–603). Berlin: De Gruyter.Google Scholar
  26. Köhler, R. (1987). Systems theoretical linguistics. Theoretical Linguistics, 14(2/3), 241–257.Google Scholar
  27. Komninos, A., & Manandhar, S. (2016). Dependency based embeddings for sentence classification tasks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1490–1500).Google Scholar
  28. Koutra, D., Parikh, A., Ramdas, A., & Xiang, J. (2011). Algorithms for graph similarity and subgraph matching. Retrieved from
  29. Koutra, D., Kang, U., Vreeken, J., & Faloutsos, C. (2014, April 24–26). VoG: summarizing and understanding large graphs. In Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA (pp. 91–99).Google Scholar
  30. Lee, K., He, L., Lewis, M., & Zettlemoyer, L. (2017). End-to-end neural coreference resolution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 188–197).Google Scholar
  31. Levelt, W. J. M. (1989). Speaking: From intention to articulation. Cambridge: MIT Press.Google Scholar
  32. Levy, O., & Goldberg, Y. (2009). Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers, pp. 302–308).Google Scholar
  33. Leydesdorff, L. (2009). The non-linear dynamics of meaning processing in social systems. Social Science Information, 48(1), 5–33.CrossRefGoogle Scholar
  34. Ling, W., Dyer, C., Black, A., & Trancoso, I. (2015). Two/too simple adaptations of word2vec for syntax problems. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Google Scholar
  35. Litman, D. (2016). Natural language processing for enhancing teaching and learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (pp. 4170–4176).Google Scholar
  36. Liu, P., & Li, Z. (2012). Task complexity: A review and conceptualization framework. International Journal of Industrial Ergonomics, 42(6), 553–568.CrossRefGoogle Scholar
  37. Liu, P., & Li, Z. (2014). Comparison of task complexity measures for emergency operating procedures: Convergent validity and predictive validity. Reliability Engineering & System Safety, 121, 289–293.CrossRefGoogle Scholar
  38. Marcus, G. (2018). Deep learning: A critical appraisal. Clinical Orthopaedics and Related Research. Retrieved from
  39. Mehler, A. (2006, June 26). In search of a bridge between network analysis in computational linguistics and computational biology—a conceptual note. In H. R. Arabnia & H. Valafar (Eds.), Proceedings of the 2006 International Conference on Bioinformatics & Computational Biology (BIOCOMP’06), Las Vegas, USA (pp. 496–500).Google Scholar
  40. Mehler, A. (2007). Compositionality in quantitative semantics. A theoretical perspective on text mining. In A. Mehler & R. Köhler (Eds.), Aspects of automatic text analysis, studies in fuzziness and soft computing (pp. 139–167). Berlin: Springer.Google Scholar
  41. Mehler, A. (2008). Structural similarities of complex networks: A computational model by example of wiki graphs. Applied Artificial Intelligence, 22(7&8), 619–683.CrossRefGoogle Scholar
  42. Mehler, A., Gleim, R., Hemati, W., & Uslu, T. (2017). Skalenfreie online soziale Lexika am Beispiel von Wiktionary. In S. Engelberg, H. Lobin, K. Steyer, & S. Wolfer (Eds.), Proceedings of 53rd Annual Conference of the Institut für Deutsche Sprache (IDS), March 14-16, Mannheim, Germany (pp. 269–291). Berlin: De Gruyter.Google Scholar
  43. Mehler, A., Hemati, W., Uslu, T., & Lücking, A. (2018a). A multidimensional model of syntactic dependency trees for authorship attribution. In J. Jiang & H. Liu (Eds.), Quantitative analysis of dependency structures (pp. 315–348). Berlin: De Gruyter.CrossRefGoogle Scholar
  44. Mehler, A., Hemati, W., Gleim, R., & Baumartz, D. (2018b). VienNA: Auf dem Weg zu einer Infrastruktur für die verteilte interaktive evolutionäre Verarbeitung natürlicher Sprache. In H. Lobin, R. Schneider, & A. Witt (Eds.), Forschungsinfrastrukturen und digitale Informationssysteme in der germanistischen Sprachwissenschaft (Vol. 6, pp. 149–176). Berlin: De Gruyter.Google Scholar
  45. Mehler, A., Gleim, R., Lücking, A., Uslu, T., & Stegbauer, C. (2018c). On the self-similarity of Wikipedia talks: A combined discourse-analytical and quantitative approach. Glottometrics, 40, 1–45.Google Scholar
  46. Mehler, A., Zlatkin-Troitschanskaia, O., Hemati, W., Molerov, D., Lücking, A., & Schmidt, S. (2018d). Integrating computational linguistic analysis of multilingual learning data and educational measurement approaches to explore student learning in higher education. In O. Zlatkin-Troitschanskaia, G. Wittum, & A. Dengel (Eds.), Positive learning in the age of information (PLATO) – A blessing or a curse? (pp. 145–193). Wiesbaden: Springer.Google Scholar
  47. Mikk, J. (1995). Methods for determining optimal readability of texts. Journal of Quantitative Linguistics, 2(2), 125–132.CrossRefGoogle Scholar
  48. Mikolov, T., Yih, W. & Zweig, G. (2013). Linguistic regularities in continuous space word representations. In Proceedings of NAACL 2013 (pp. 746–751).Google Scholar
  49. Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.CrossRefGoogle Scholar
  50. Nisbett, R. E. (2003). The geography of thought: How Asians and Westerners think differently … and why. New York: Free Press.Google Scholar
  51. Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27, 169–226.Google Scholar
  52. Ramesh, V. (1995). Performance characterization of image understanding algorithms. PhD thesis, Department of Electrical Engineering, University of Washington, Seattle.Google Scholar
  53. Rieger, B. (1985). Semantische Dispositionen: Prozedurale Wissensstrukturen mit stereotypisch repräsentierten Wortbedeutungen. In B. Rieger (Ed.), Dynamik in der Bedeutungskonstitution, Papiere zur Textlinguistik (Vol. Bd. 46, pp. 163–228). Hamburg: Buske.Google Scholar
  54. Rieger, B. (2001). Computing granular word meanings: A fuzzy linguistic approach in computational semiotics. In P. Wang (Ed.), Computing with words (pp. 147–208). New York: Wiley.Google Scholar
  55. Sag, I. A., Baldwin, T., Bond, F., Copestake, A., & Flickinger, D. (2002). Multiword expressions: A pain in the neck for NLP. In Proceedings of the 3rd. International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2002, pp. 1–15).Google Scholar
  56. Schnotz, W. (1994). Aufbau von Wissensstrukturen: Untersuchungen zur Kohärenzbildung beim Wissenserwerb mit Texten. Beltz, Weinheim.Google Scholar
  57. Silver, D. L., & Bennett, K. P. (2008). Guest editor’s introduction: Special issue on inductive transfer learning. Machine Learning, 73(3), 215.CrossRefGoogle Scholar
  58. Stouten, H., & Größler, A. (2017). Task complexity in individual stock control tasks for laboratory experiments on human understanding of dynamic systems. Systems Research and Behavioral Science, 34(1), 62–77.CrossRefGoogle Scholar
  59. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.CrossRefGoogle Scholar
  60. Sweller, J. (2003). Evolution of human cognitive architecture. Psychology of Learning and Motivation, 43, 215.CrossRefGoogle Scholar
  61. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory (Explorations in the learning sciences, instructional systems and performance technologies). New York: Springer.CrossRefGoogle Scholar
  62. Tuldava, J. (1993). The statistical structure of a text and its readability. In L. Hřebíček & G. Altmann (Eds.), Quantitative text analysis (pp. 215–227). Trier: Wissenschaftlicher Verlag.Google Scholar
  63. Uslu, T., Mehler, A., Niekler, A., & Baumartz, D. (2018). Towards a DDC-based topic network model of Wikipedia. In Proceedings of 2nd International Workshop on Modeling, Analysis, and Management of Social Networks and their Applications (SOCNET 2018).Google Scholar
  64. Van Dijk, T. A., & Kintsch, W. (1983). Strategies of discourse comprehension. New York: Academic Press.Google Scholar
  65. Van Trijp, R., Steels, L., Beuls, K., & Wellens, P. (2012). Fluid construction grammar: The new kid on the block. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 63–68).Google Scholar
  66. Veeravasarapu, VSR, Rothkopf, C., & Ramesh, V. (2017a). Model-driven simulations for computer vision. In Applications of Computer Vision (WACV), 2017 IEEE Winter Conference (pp. 1063–1071).Google Scholar
  67. Veeravasarapu, VSR, Rothkopf, C., & Ramesh, V. (2017b). Adversarially tuned scene generation. In IEEE CVPR (pp. 6441–6449).Google Scholar
  68. Weis, T., Mundt, M., Harding, P., & Ramesh, V. (2017). Anomaly detection for automotive visual signal transition estimation. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference (pp. 1–8).Google Scholar
  69. Wharton, C., & Kintsch, W. (1991). An overview of construction-integration model: A theory of comprehension as a foundation for a new cognitive architecture. ACM SIGART Bulletin, 2(4), 169–173.CrossRefGoogle Scholar
  70. Yablo, S. (2014). Aboutness. Princeton: Princeton University Press.CrossRefGoogle Scholar
  71. Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111–127.CrossRefGoogle Scholar
  72. Zhang, Q., & Zhu, S.-C. (2018). Visual interpretability for deep learning: A survey. Clinical Orthopaedics and Related Research. Retrieved from
  73. Zipf, G. K. (1972). Human behavior and the principle of least effort. an introduction to human ecology. New York: Hafner.Google Scholar
  74. Zlatkin-Troitschanskaia, O., Förster, M., Brückner, S., & Happ, R. (2014). Insights from a German assessment of business and economics competence. In H. Coates (Ed.), Higher education learning outcomes assessment - International perspectives (pp. 175–197). Frankfurt am Main: Peter Lang.Google Scholar
  75. Zlatkin-Troitschanskaia, O., Schmidt, S., Molerov, D., Shavelson, R. J., & Berliner, D. (2018). Conceptual fundamentals for a theoretical and empirical framework of positive learning. In O. Zlatkin-Troitschanskaia, G. Wittum, & A. Dengel (Eds.), Positive learning in the age of information – A blessing or a curse? (pp. 29–50). Wiesbaden: Springer.Google Scholar

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Authors and Affiliations

  1. 1.Department of Computer Science and MathematicsGoethe University Frankfurt am MainFrankfurt am MainGermany
  2. 2.Department of Computer Science and MathematicsGoethe University Frankfurt am MainFrankfurt am MainGermany

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