Intelligent Text Processing to Help Readers with Autism

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.

[1]  Lucia Specia Translating from Complex to Simplified Sentences , 2010, PROPOR.

[2]  Joakim Nivre,et al.  Analyzing and Integrating Dependency Parsers , 2011, CL.

[3]  Maxine Eskénazi,et al.  An Open Corpus of Everyday Documents for Simplification Tasks , 2014, PITR@EACL.

[4]  Ricardo Baeza-Yates,et al.  Simplify or help?: text simplification strategies for people with dyslexia , 2013, W4A.

[5]  W. E. Bosma Image Retrieval Supports Multimedia Authoring , 2005 .

[6]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[7]  Horacio Saggion,et al.  Can Spanish Be Simpler? LexSiS: Lexical Simplification for Spanish , 2012, COLING.

[8]  Advaith Siddharthan,et al.  Syntactic Simplification and Text Cohesion , 2006 .

[9]  David Kauchak,et al.  Simple English Wikipedia: A New Text Simplification Task , 2011, ACL.

[10]  Chris Callison-Burch,et al.  Problems in Current Text Simplification Research: New Data Can Help , 2015, TACL.

[11]  Chaleece Sandberg,et al.  Effects of syntactic complexity, semantic reversibility, and explicitness on discourse comprehension in persons with aphasia and in healthy controls. , 2012, American journal of speech-language pathology.

[12]  Tomás Jelínek Improvements to Dependency Parsing Using Automatic Simplification of Data , 2014, LREC.

[13]  Tadashi Nomoto,et al.  Lexico-syntactic text simplification and compression with typed dependencies , 2014, COLING.

[14]  Qing Zeng-Treitler,et al.  A semantic and syntactic text simplification tool for health content. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[15]  Sanja Štajner,et al.  New data-driven approaches to text simplification , 2015 .

[16]  Noémie Elhadad Comprehending Technical Texts: Predicting and Defining Unfamiliar Terms , 2006, AMIA.

[17]  Richard Evans,et al.  A Tagging Approach to Identify Complex Constituents for Text Simplification , 2013, RANLP.

[18]  N. Minshew,et al.  Autism as a disorder of complex information processing , 1998 .

[19]  Daphne Koller,et al.  Sentence Simplification for Semantic Role Labeling , 2008, ACL.

[20]  Horacio Saggion,et al.  Can Numerical Expressions Be Simpler? Implementation and Demostration of a Numerical Simplification System for Spanish , 2014, LREC.

[21]  Richard Evans,et al.  An evaluation of syntactic simplification rules for people with autism , 2014, PITR@EACL.

[22]  Mirella Lapata,et al.  Sentence Compression as Tree Transduction , 2009, J. Artif. Intell. Res..

[23]  Yannick Versley,et al.  SemEval-2010 Task 1: Coreference Resolution in Multiple Languages , 2009, *SEMEVAL.

[24]  Luis Alfonso Ureña López,et al.  Language technologies applied to document simplification for helping autistic people , 2015, Expert Syst. Appl..

[25]  Andrew D. Walker,et al.  Investigation into Human Preference between Common and Unambiguous Lexical Substitutions , 2011, ENLG.

[26]  Hsin-Hsi Chen,et al.  A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications , 2012, COLING.

[27]  Rada Mihalcea,et al.  Wikify!: linking documents to encyclopedic knowledge , 2007, CIKM '07.

[28]  H. Scarborough Index of Productive Syntax , 1990, Applied Psycholinguistics.

[29]  cationR. Chandrasekar Automatic Induction of Rules for Text Simpli , 1997 .

[30]  Alla Keselman,et al.  Making Texts in Electronic Health Records Comprehensible to Consumers: A Prototype Translator , 2007, AMIA.

[31]  Uta Frith,et al.  Reading for meaning and reading for sound in autistic and dyslexic children , 1983 .

[32]  Regina Barzilay,et al.  Sentence Alignment for Monolingual Comparable Corpora , 2003, EMNLP.

[33]  Dipti Misra Sharma,et al.  Exploring the effects of Sentence Simplification on Hindi to English Machine Translation System , 2014 .

[34]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[35]  Gustavo Paetzold Reliable Lexical Simplification for Non-Native Speakers , 2015, HLT-NAACL.

[36]  Yvonne Margaret Canning,et al.  Syntactic simplification of text , 2002 .

[37]  Richard Evans,et al.  Six Good Predictors of Autistic Text Comprehension , 2015, RANLP.

[38]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[39]  Perry D. Klein,et al.  Exploration of Strategies for Facilitating the Reading Comprehension of High-Functioning Students with Autism Spectrum Disorders , 2004, Journal of autism and developmental disorders.

[40]  Emiel Krahmer,et al.  Sentence Simplification by Monolingual Machine Translation , 2012, ACL.

[41]  C. K. Ogden,et al.  Basic English : a general introduction with rules and grammar , 1930 .

[42]  Heeyoung Lee,et al.  Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task , 2011, CoNLL Shared Task.

[43]  Iryna Gurevych,et al.  A Monolingual Tree-based Translation Model for Sentence Simplification , 2010, COLING.

[44]  Georgi Georgiev,et al.  Combining POS Tagging, Dependency Parsing and Coreferential Resolution for Bulgarian , 2013, RANLP.

[45]  Tomoyuki Kajiwara,et al.  Evaluation Dataset and System for Japanese Lexical Simplification , 2015, ACL.

[46]  A. Hasan,et al.  Organisation for Economic Co-operation and Development , 2007 .

[47]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[48]  Lawrence Hunter,et al.  Extracting Molecular Binding Relationships from Biomedical Text , 2000, ANLP.

[49]  Eduard Barbu,et al.  Open Book: a tool for helping ASD users’ semantic comprehension , 2013 .

[50]  Noémie Elhadad,et al.  Putting it Simply: a Context-Aware Approach to Lexical Simplification , 2011, ACL.

[51]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[52]  Richard J. Evans,et al.  Comparing methods for the syntactic simplification of sentences in information extraction , 2011, Literary and Linguistic Computing.

[53]  Kate Nation,et al.  Patterns of Reading Ability in Children with Autism Spectrum Disorder , 2006, Journal of autism and developmental disorders.

[54]  Susan Purdon,et al.  Autism spectrum disorders in adults living in households throughout England: Report from the adult psychiatric morbidity survey 2007 , 2009 .

[55]  Advaith Siddharthan,et al.  Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies , 2011, ENLG.

[56]  Raman Chandrasekar,et al.  Automatic induction of rules for text simplification , 1997, Knowl. Based Syst..