Skip to main content
SpringerLink
Log in

Intelligent Text Processing to Help Readers with Autism

  • Chapter
  • First Online:
Intelligent Natural Language Processing: Trends and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder which has a life-long impact on the lives of people diagnosed with the condition. In many cases, people with ASD are unable to derive the gist or meaning of written documents due to their inability to process complex sentences, understand non-literal text, and understand uncommon and technical terms. This paper presents FIRST, an innovative project which developed language technology (LT) to make documents more accessible to people with ASD. The project has produced a powerful editor which enables carers of people with ASD to prepare texts suitable for this population. Assessment of the texts generated using the editor showed that they are not less readable than those generated more slowly as a result of onerous unaided conversion and were significantly more readable than the originals. Evaluation of the tool shows that it can have a positive impact on the lives of people with ASD.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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.

    http://openbook.net.

  2. 2.

    http://behavenet.com/apa-diagnostic-classification-dsm-iv-tr.

  3. 3.

    http://msdn.microsoft.com/en-us/library/windows/desktop/aa511258.aspx.

  4. 4.

    http://msdn.microsoft.com/en-us/library/windows/apps/hh779072.aspx.

  5. 5.

    A demo of the English sign tagger is available at http://rgcl.wlv.ac.uk/demos/SignTaggerWebDemo/.

  6. 6.

    Measured using Scarborough’s index of productive syntax [50].

  7. 7.

    http://atlasti.com/.

References

  1. Angrosh, M., Nomoto, T., Siddharthan, A.: Lexico-syntactic text simplification and compression with typed dependencies. In: Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers, COLING 2014, pp. 1996–2006 (2014)

    Google Scholar 

  2. Barbu, E., Martín-Valdivia, M., Alfonso, L., Lopez, U.: Open book: a tool for helping ASD users’ semantic comprehension. In: Proceedings of the 2th Workshop of Natural Language Processing for Improving Textual Accessibility (NLP4ITA), pp. 11–19. Atlanta, US (2013)

    Google Scholar 

  3. Barbu, E., Martín-Valdivia, M.T., Martínez-Cámara, E., López, L.A.U.: Language technologies applied to document simplification for helping autistic people. Expert Syst. Appl. Int. J. 42(12), 5076–5086 (2015)

    Google Scholar 

  4. Barzilay, R., Elhadad, N.: Sentence alignment for monolingual comparable corpora. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP’03), pp. 25–32. Sapporo, Japan (2003)

    Google Scholar 

  5. Bautista, S., Saggion, H.: Can numerical expressions be simpler? Implementation and demonstration of a numerical simplification system for Spanish. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014) (2014)

    Google Scholar 

  6. Biran, O., Brody, S., Elhadad, N.: Putting it simply: a context-aware approach to lexical simplification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: short papers (ACL-2011), pp. 496–501. Portland, Oregon (2011)

    Google Scholar 

  7. Bosma, W.: Image Retrieval Supports Multimedia Authoring, pp. 89–94. ITC-irst, Trento, Italy (2005)

    Google Scholar 

  8. Bott, S., Rello, L., Drndarevic, B., Saggion, H.: Can spanish be simpler? LexSiS: Lexical simplification for Spanish. In: Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics, Lecture Notes in Computer Science, pp. 8–15. Springer, Samos, Greece (2012)

    Google Scholar 

  9. Brugha, T., McManus, S., Meltzer, H., Smith, J., Scott, F.J., Purdon, S.: Autism spectrum disorders in adults living in households throughout England. Report from the Adult Psychiatric Morbidity Survey (2007)

    Google Scholar 

  10. Canning, Y.: Syntactic simplification of text. Ph.D. Thesis, University of Sunderland (2002)

    Google Scholar 

  11. Chandrasekar, R., Srinivas, B.: Automatic induction of rules for text simplification. Knowl.-Based Syst. 10, 183–190 (1997)

    Article  Google Scholar 

  12. Chen, H.B., Huang, H.H., Chen, H.H., Tan, C.T.: A simplification-translation-restoration framework for cross-domain SMT applications. Proc. COLING 2012, 545–560 (2012)

    Google Scholar 

  13. Cohn, T., Lapata, M.: Sentence compression as tree transduction. J. Artif. Intell. Res. 20(34), 637–74 (2009)

    MATH  Google Scholar 

  14. Coster, W., Kauchak, D.: Simple English Wikipedia: a new text simplification task. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL-2011), pp. 665–669. Portland, Oregon (2011)

    Google Scholar 

  15. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A., Damljanovic, D., Heitz, T., Greenwood, M.A., Saggion, H., Petrak, J., Li, Y., Peters, W.: Text Processing with GATE (Version 6). http://tinyurl.com/gatebook (2011)

  16. De Belder, J., Deschacht, K., Moens, M.F.: Lexical simplification. In: Proceedings of the 1st International Conference on Interdisciplinary Research on Technology, Education, and Communication (ITEC-2010). Kortrijk, Belgium (2010)

    Google Scholar 

  17. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)

    Google Scholar 

  18. Dornescu, I., Evans, R., Orasan, C.: A tagging approach to identify complex constituents for text simplification. In: Proceedings of Recent Advances in Natural Language Processing, pp. 221–229. Hissar, Bulgaria (2013)

    Google Scholar 

  19. Elhadad, N.: Comprehending technical texts: predicting and defining unfamiliar terms. In: AMIA Annual Symposium Proceedings, pp. 239–243 (2006)

    Google Scholar 

  20. Erkan, G., Radev, D.R.: Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22(1), 457–479 (2004)

    Google Scholar 

  21. Evans, R.: Comparing methods for the syntactic simplification of sentences in information extraction. Lit. Linguist. Comput. 26(4), 371–388 (2011)

    Article  Google Scholar 

  22. Evans, R., Orasan, C., Dornescu, I.: An evaluation of syntactic simplification rules for people with autism. In: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), pp. 131–140. Gothenburg, Sweden (2014)

    Google Scholar 

  23. Frith, U., Snowling, M.: Reading for meaning and reading for sound in autistic and dyslexic children. J. Dev. Psychol. 1, 329–342 (1983)

    Article  Google Scholar 

  24. González-Navarro, A., Freire-Prudencio, S., Gil, D., Martos-Pérez, J., Jordanova, V., Cerga-Pashoja, A., Shishkova, A., Evans, R.: FIRST: una herramienta para facilitar la comprensión lectora en el trastorno del espectro autista de alto funcionamiento. Rev. Neurol. 58(Supl 1), 129–135 (2014)

    Google Scholar 

  25. Granizo, L., Naylor, P., del Barrio, C.: Analysis of the social relationships of pupils with Asperger syndrome in mainstream secondary schools: case studies. Rev. Psicodidactica 281–292 (2006)

    Google Scholar 

  26. Jelínek, T.: Improvements to dependency parsing using automatic simplification of data. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014) (2014)

    Google Scholar 

  27. Jordanova, V., Cerga Pashoja, A.: Effects of syntactic complexity, semantic reversibility, and explicitness on discourse comprehension in persons with aphasia and in healthy controls. BAOJ Psychol. 1(003) (2016)

    Google Scholar 

  28. Jordanova, V., Evans, R., Cerga Pashoja, A.: D7.8: Final evaluation report. Technical Report, FIRST_D7.8_20141119, The FIRST Consortium (2014)

    Google Scholar 

  29. Kajiwara, T., Yamamoto, K.: Evaluation dataset and system for Japanese lexical simplification. In: Proceedings of the ACL-IJCNLP 2015 Student Research Workshop, pp. 35–40 (2015)

    Google Scholar 

  30. Kandula, S., Curtis, D., Zeng-Treitler, Q.: A semantic and syntactic text simplification tool for health content. In: AMIA Annual Symposium Proceedings, pp. 366–370 (2010)

    Google Scholar 

  31. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann (2001)

    Google Scholar 

  32. Lee, H., Peirsman, Y., Chang, A., Chambers, N., Surdeanu, M., Jurafsky, D.: Stanford’s multi-pass sieve coreference resolution system at the CoNLL-2011 shared task. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL Shared Task ’11, pp. 28–34. Association for Computational Linguistics, Stroudsburg, PA, USA (2011)

    Google Scholar 

  33. Lloret, E., Moreda, P., Moreno, I., Canales, L.: D4.1: Meaning Disambiguator: v2.1. Technical Report, FIRST_D4.1_20140531, The FIRST Consortium (2014)

    Google Scholar 

  34. Martos, J., Freire, S., González, A., Gil, D., Evans, R., Jordanova, V., Cerga, A., Shishkova, A., Orasan, C.: User preferences: updated. Techbical Report D2.2, Deletrea, Madrid, Spain (2013)

    Google Scholar 

  35. Max, A.: Syntactic simplification—an application to text for aphasic readers. M.Phil in computer speech and language processing, University of Cambridge, Wolfson College (2000)

    Google Scholar 

  36. McDonald, R.T., Nivre, J.: Analyzing and integrating dependency parsers. Comput. Linguist. 37(1), 197–230 (2011)

    Article  Google Scholar 

  37. Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. CIKM 7, 233–242 (2007)

    Google Scholar 

  38. Mihalcea, R., Tarau, P.: Textrank: bringing order into texts. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, pp. 404–411. Association for Computational Linguistics, Barcelona, Spain (2004)

    Google Scholar 

  39. Minshew, N., Goldstein, G.: Autism as a disorder of complex information processing. Ment. Retard. Dev. Disabil. Res. Rev. 4, 129–136 (1998)

    Article  Google Scholar 

  40. Mishra, K., Soni, A., Sharma, R., Sharma, D.: Exploring the effects of sentence simplification on Hindi to English machine translation system. In: Proceedings of the Workshop on Automatic Text Simplification—Methods and Applications in the Multilingual Society (ATS-MA 2014), pp. 21–29 (2014)

    Google Scholar 

  41. Nation, K., Clarke, P., Wright, B., Williams, C.: Patterns of reading ability in children with autism-spectrum disorder. J. Autism Dev. Disord. 36, 911–919 (2006)

    Article  Google Scholar 

  42. O’Connor, I.M., Klein, P.D.: Exploration of strategies for facilitating the reading comprehension of high-functioning students with autism spectrum disorders. J. Autism Dev. Disord. 34(2), 115–127 (2004)

    Article  Google Scholar 

  43. OECD: Literacy in the information age: final report of the international adult literacy survey. Technical Report, Organisation for Economic Co-operation and Development, Paris, France (2000)

    Google Scholar 

  44. Ogden, C.K.: Basic English: A General Introduction with Rules and Grammar. In: Paul, K. (ed.) Trench, Trubner & Co., Ltd., London (1932)

    Google Scholar 

  45. Paetzold, G.: Reliable lexical simplification for non-native speakers. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pp. 9–16 (2015)

    Google Scholar 

  46. Pellow, D., Eskenazi, M.: An open corpus of everyday documents for simplification tasks. In: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), pp. 84–93. Gothenburg, Sweden (2014)

    Google Scholar 

  47. Recasens, M., Màrquez, L., Sapena, E., Martí, M.A., Taulé, M., Hoste, V., Poesio, M., Versley, Y.: Semeval-2010 task 1: Coreference resolution in multiple languages. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval@ACL 2010, pp. 1–8. Uppsala, Sweden (2010)

    Google Scholar 

  48. Rello, L., Baeza-Yates, R., Bott, S., Saggion, H.: Simplify or help?: Text simplification strategies for people with dyslexia. In: Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility, pp. 15:1–15:10. New York, NY, USA (2013)

    Google Scholar 

  49. Rindflesch, T.C., Rajan, J.V., Hunter, L.: Extracting molecular binding relationships from biomedical text. In: Proceedings of the sixth conference on Applied natural language processing, pp. 188–195. Seattle, Washington (2000)

    Google Scholar 

  50. Scarborough, H.S.: Index of productive syntax. Appl. Psycholinguist. 11, 1–22 (1990)

    Article  Google Scholar 

  51. Siddharthan, A.: Syntactic simplification and text cohesion. Res. Lang. Comput. 4(1), 77–109 (2006)

    Article  Google Scholar 

  52. Siddharthan, A.: Text simplification using typed dependencies: a comparison of the robustness of different generation strategies. In: Proceedings of the 13th European Workshop on Natural Language Generation, pp. 2–11 (2011)

    Google Scholar 

  53. Specia, L.: Translating from complex to simplified sentences. In: Proceedings of the Conference on Computational Processing of the Portuguese Language, pp. 30–39. Springer, Porto Alegre, RS, Brazil (2010)

    Google Scholar 

  54. Štajner, S.: New data-driven approaches to text simplification. Ph.D. Thesis, University of Wolverhampton (2015)

    Google Scholar 

  55. Sutton, C., McCallum, A.: An introduction to conditional random fields (2010). arXiv:1011.4088

  56. Vickrey, D., Koller, D.: Sentence simplification for semantic role labeling. In: Proceedings of ACL-08: HLT, pp. 344–352 (2008)

    Google Scholar 

  57. Walker, A., Siddharthan, A., Starkey, A.: Investigation into human preference between common and unambiguous lexical substitutions. In: Proceedings of the 13th European Workshop on Natural Language Generation (ENLG), pp. 176–180. Nancy, France (2011)

    Google Scholar 

  58. Wubben, S., van den Bosch, A., Krahmer, E.: Sentence simplification by monolingual machine translation. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-12), pp. 1015–1024. Jeju, Republic of South Korea (2012)

    Google Scholar 

  59. Xu, W., Callison-Burch, C., Napoles, C.: Problems in current text simplification research: new data can help. Trans. Assoc. Comput. Linguist. 3, 283–297 (2015)

    Google Scholar 

  60. Yaneva, V., Evans, R.: Six good predictors of autistic text comprehension. In: Proceedings of Recent Advances in Natural Language Processing (RANLP 2015), pp. 697–706. Hissar, Bulgaria (2015)

    Google Scholar 

  61. Zeng-Treitler, Q., Goryachev, S., Kim, H., Keselman, A., Rosendale, D.: Making texts in electronic health records comprehensible to consumers: a prototype translator. In: AMIA Annual Symposium Proceedings, pp. 846–850 (2007)

    Google Scholar 

  62. Zhikov, V., Georgiev, G., Simov, K., Osenova, P.: Combining POS tagging, dependency parsing and co-referential resolution for Bulgarian. In: Proceedings of RANLP’13. Bulgaria (2013)

    Google Scholar 

  63. Zhu, Z., Bernhard, D., Gurevych, I.: A monolingual tree-based translation model for sentence simplification. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), pp. 1353–1361. Beijing, China (2010)

    Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the contribution of all the partners to the project. This paper would not have been possible without their contribution to the various stages of the research carried out. We would also like to thank the carers and individuals with high-functioning autism who participated in the different evaluations reported in this paper. The research was partially funded by the EC under the 7th Framework Programme for Research and Technological Development (FP7- ICT-2011.5.5 FIRST 287607).

Author information

Authors and Affiliations

  1. Research Institute in Information and Language Processing, University of Wolverhampton, Wolverhampton, WV1 1LY, UK

    Constantin Orăsan, Richard Evans & Ruslan Mitkov

Authors
  1. Constantin Orăsan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruslan Mitkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Constantin Orăsan .

Editor information

Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Orăsan, C., Evans, R., Mitkov, R. (2018). Intelligent Text Processing to Help Readers with Autism. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67056-0_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67055-3

  • Online ISBN: 978-3-319-67056-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation