Skip to main content
SpringerLink
Log in

A Computational Measure for the Semantic Readability of Segmented Texts

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13377))

Included in the following conference series:

Abstract

In this paper we introduce a computational procedure for measuring the semantic readability of a segmented text. The procedure mainly consists of three steps. First, natural language processing tools and unsupervised machine learning techniques are adopted in order to obtain a vectorized numerical representation for any section or segment of the inputted text. Hence, similar or semantically related text segments are modeled by nearby points in a vector space, then the shortest and longest Hamiltonian paths passing through them are computed. Lastly, the lengths of these paths and that of the original ordering on the segments are combined into an arithmetic expression in order to derive an index, which may be used to gauge the semantic difficulty that a reader is supposed to experience when reading the text. A preliminary experimental study is conducted on seven classic narrative texts written in English, which were obtained from the well-known Gutenberg project. The experimental results appear to be in line with our expectations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    glove-wiki-gigaword-100 is available with the Python module Gensim [17] at the url https://github.com/RaRe-Technologies/gensim-data.

  2. 2.

    Concorde is available from https://www.math.uwaterloo.ca/tsp/concorde.html.

References

  1. Applegate, D., Bixby, R., Chvátal, V., Cook, W.: TSP cuts which do not conform to the template paradigm. In: Jünger, M., Naddef, D. (eds.) Computational Combinatorial Optimization. LNCS, vol. 2241, pp. 261–303. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45586-8_7

    Chapter  Google Scholar 

  2. Baioletti, M., Milani, A., Santucci, V., Bartoccini, U.: An experimental comparison of algebraic differential evolution using different generating sets. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2019, pp. 1527–1534. (2019). https://doi.org/10.1145/3319619.3326854

  3. Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S., Ridgeway, G.: Clustering on the unit hypersphere using von Mises-Fisher distributions. J. Mach. Learning Res. 6(9), 1345–1382 (2005)

    MathSciNet  MATH  Google Scholar 

  4. Barvinok, A., Gimadi, E.K., Serdyukov, A.I.: The maximum TSP. In: Gutin, G., Punnen, A.P. (eds.) The Traveling Salesman Problem and Its Variations. Combinatorial Optimization, vol. 12, pp. 585–607. Springer, Boston (2007). https://doi.org/10.1007/0-306-48213-4_12

    Chapter  MATH  Google Scholar 

  5. Calfee, R.C., Curley, R.: Structures of prose in content areas. In: Understanding Reading Comprehension, pp. 161–180 (1984)

    Google Scholar 

  6. Chowdhary, K.: Natural language processing. Fundamentals of Artificial Intelligence, pp. 603–649 (2020)

    Google Scholar 

  7. Church, K.W.: Word2vec. Nat. Lang. Eng. 23(1), 155–162 (2017)

    Article  Google Scholar 

  8. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  9. Dieng, A.B., Ruiz, F.J., Blei, D.M.: Topic modeling in embedding spaces. Trans. Assoc. Comput. Linguis. 8, 439–453 (2020)

    Article  Google Scholar 

  10. DuBay, W.H.: The principles of readability. Online Submission (2004)

    Google Scholar 

  11. Forti, L., Grego Bolli, G., Santarelli, F., Santucci, V., Spina, S.: MALT-IT2: a new resource to measure text difficulty in light of CEFR levels for Italian L2 learning. In: Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France, May 2020, pp. 7204–7211. European Language Resources Association (2020). https://aclanthology.org/2020.lrec-1.890

  12. Forti, L., Milani, A., Piersanti, L., Santarelli, F., Santucci, V., Spina, S.: Measuring text complexity for Italian as a second language learning purposes. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy, August 2019, pp. 360–368. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/W19-4438

  13. Gourru, A., Guille, A., Velcin, J., Jacques, J.: Document network projection in pretrained word embedding space. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 150–157. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_19

    Chapter  Google Scholar 

  14. Graff, D., Kong, J., Chen, K., Maeda, K.: English Gigaword. Linguis. Data Consortium Philadelphia 4(1), 34 (2003)

    Google Scholar 

  15. Jones, M.J., Shoemaker, P.A.: Accounting narratives: a review of empirical studies of content and readability. J. Acc. Lit. 13, 142 (1994)

    Google Scholar 

  16. Jünger, M., Reinelt, G., Rinaldi, G.: The traveling salesman problem. In: Handbooks in Operations Research and Management Science, vol. 7, pp. 225–330 (1995)

    Google Scholar 

  17. Khosrovian, K., Pfahl, D., Garousi, V.: GENSIM 2.0: a customizable process simulation model for software process evaluation. In: Wang, Q., Pfahl, D., Raffo, D.M. (eds.) ICSP 2008. LNCS, vol. 5007, pp. 294–306. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79588-9_26

    Chapter  Google Scholar 

  18. Kwolek, W.F.: A readability survey of technical and popular literature. Journalism Q. 50(2), 255–264 (1973). https://doi.org/10.1177/107769907305000206

    Article  Google Scholar 

  19. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine learning, pp. 1188–1196. PMLR (2014)

    Google Scholar 

  20. Li, B., Han, L.: Distance weighted cosine similarity measure for text classification. In: Yin, H., et al. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 611–618. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_74

    Chapter  Google Scholar 

  21. Li, Y., Yang, T.: Word embedding for understanding natural language: a survey. In: Srinivasan, S. (ed.) Guide to Big Data Applications. SBD, vol. 26, pp. 83–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-53817-4_4

    Chapter  Google Scholar 

  22. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  23. Rahman, M.S., Kaykobad, M.: On Hamiltonian cycles and Hamiltonian paths. Inf. Process. Lett. 94(1), 37–41 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Ruder, S., Peters, M.E., Swayamdipta, S., Wolf, T.: Transfer learning in natural language processing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pp. 15–18 (2019)

    Google Scholar 

  25. Santucci, V., Baioletti, M., Milani, A.: An algebraic differential evolution for the linear ordering problem. In: Companion Material Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2015, pp. 1479–1480 (2015). https://doi.org/10.1145/2739482.2764693

  26. Santucci, V., Ceberio, J.: Using pairwise precedences for solving the linear ordering problem. Appl. Soft Comput. 87, 105998 (2020). https://doi.org/10.1016/j.asoc.2019.105998

    Article  Google Scholar 

  27. Santucci, V., Forti, L., Santarelli, F., Spina, S., Milani, A.: Learning to classify text complexity for the Italian language using support vector machines. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12250, pp. 367–376. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58802-1_27

    Chapter  Google Scholar 

  28. Santucci, V., Santarelli, F., Forti, L., Spina, S.: Automatic classification of text complexity. Appl. Sci. 10(20) (2020). https://doi.org/10.3390/app10207285, https://www.mdpi.com/2076-3417/10/20/7285

  29. Santucci, V., Spina, S., Milani, A., Biondi, G., Di Bari, G.: Detecting hate speech for Italian language in social media. In: EVALITA 2018, Co-located with the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018), vol. 2263 (2018)

    Google Scholar 

  30. Schnabel, T., Labutov, I., Mimno, D., Joachims, T.: Evaluation methods for unsupervised word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 298–307 (2015)

    Google Scholar 

  31. Smith, E.A., Kincaid, J.P.: Derivation and validation of the automated readability index for use with technical materials. Hum. Factors 12(5), 457–564 (1970). https://doi.org/10.1177/001872087001200505

    Article  Google Scholar 

  32. Stroube, B.: Literary freedom: project Gutenberg. XRDS: Crossroads, ACM Mag. Students 10(1), 3–3 (2003)

    Article  Google Scholar 

  33. Yeoh, J.M., Caraffini, F., Homapour, E., Santucci, V., Milani, A.: A clustering system for dynamic data streams based on metaheuristic optimisation. Mathematics 7(12), 1229 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University for Foreigners of Perugia, Perugia, Italy

    Valentino Santucci, Umberto Bartoccini & Fabio Zanda

  2. School of Communication and Film, Hong Kong Baptist University, Hong Kong, China

    Paolo Mengoni

Authors
  1. Valentino Santucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Umberto Bartoccini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Mengoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabio Zanda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentino Santucci .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Santucci, V., Bartoccini, U., Mengoni, P., Zanda, F. (2022). A Computational Measure for the Semantic Readability of Segmented Texts. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10536-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10535-7

  • Online ISBN: 978-3-031-10536-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation