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Ph.D. Presentation Title: DysWebxia: A Text Accessibility Model for People with Dyslexia Author: Luz Rello Advisors: Ricardo Baeza-Yates and Horacio Saggion Abstract: Worldwide, 10% of the population has dyslexia, a cognitive disability that reduces readability and comprehension of written information. The goal of this thesis is to make text more accessible for people with dyslexia by combining human computer interaction validation methods and natural language processing techniques. In the initial phase of this study we examined how people with dyslexia identify errors in written text. Their written errors were analyzed and used to estimate the presence of text written by individuals with dyslexia in the Web. After concluding that dyslexic errors relate to presentation and content features of text, we carried out a set of experiments using eye tracking to determine the conditions that led to improved readability and comprehension. After finding the relevant parameters for text presentation and content modification, we implemented a lexical simplification system. Finally, the results of the investigation and the resources created, lead to a model, DysWebxia, that proposes a set of recommendations that have been successfully integrated in four applications.
Citation preview
Outline
Ricardo Baeza-Yates Web Research Group
Universitat Pompeu Fabra & Yahoo Labs Barcelona
DysWebxia: A Text Accessibility Model for People with Dyslexia
Advisors:
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Luz Rello
Horacio Saggion Natural Language Processing Group
Universitat Pompeu Fabra Barcelona
OutlineOutline
— What? !— Why?
— Goal !— Motivation — Understanding
— Text Presentation
— Text Content
— Integration— How?
— Methodology
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Applications
OutlineMain Goal
Improve Digital Accessibility
People with Dyslexia
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineSecondary Goals
— To have a deeper understanding of dyslexia by analyzing how people with dyslexia read and write, using their misspelling errors as a starting point.
!— To find out the best text presentation parameters which benefit the reading performance –readability and comprehension– of people with dyslexia.
!— To find out the text content modifications that benefit the reading performance of people with dyslexia.
!— To propose a set of recommendations combining the positive results, and integrate them in reading applications for people with dyslexia.
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineWhy?
Dyslexia is a learning disability characterized by difficulties with accurate word recognition and by poor spelling and decoding abilities !!!As side effect, this impedes the growth of vocabulary and background knowledge. Children with dyslexia tend to show signs of depression and low self-esteem
[Vellutino et al., 2004]
[International Association of
Dyslexia, 2011][Shaywitz, 2008]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
— Neurological origin
— Language specific manifestations
— 8.6% in Spanish (Canary Islands)
— 11.8% in Spanish (Murcia)
— 10 - 17.5% of the USA population
— 10.8% English speaking children
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
— Most frequent signal
— 15.2% in Europe
— 25% in Spain
— 4 of 6 cases are related to dyslexia
Frequent !!!!!Universal !!!!School Failure
Dyslexia
[International Dyslexia Association, 2011]
[European Commission, 2011]
[Eurostat, 2011]
[Spanish Federation of Dyslexia, 2008]
[Vellutino et al., 2004]
[Brunswick, 2010]
[Jiménez et al. 2009]
[Carrillo et al. 2011]
[National Academy of Sciences, 1987]
[Shaywitz et al. 1992]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
— Information access
— Information democratization
— Benefits people without dyslexia
— Benefits others users, e.g. low vision
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
— Digital format
— eBook sales increased by
115.8% (January 2011)
Human Right !!!!Good for Dyslexia, Useful for All !!!Right Moment
Dyslexia
[Dixon, 2007][McCarthy & Swierenga,
2010]
[Evett & Brown, 2005]
[United Nations Committee of the General Assembly, 2006]
[Association of American Publishers, 2011]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Which problems dyslexic people experience?
Are there linguistic foundations?
Linguistics
Cognitive Neuroscience
Natural Language Processing
How NLP could help dyslexic people?
How text presentation could help people with dyslexia?
Human Computer Interaction
Eye-trackingHow can we measure the reading performance?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Eye-trackingHow can we measure the reading performance?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineHow Do We Read? Eye Tracking!
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Every dot is a fixation point
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
https://www.youtube.com/watch?v=P1dRqpRi4csSee VIDEO here:
OutlineMethodology - Participants, Equipment
Participants with Dyslexia Control Group
— From 23 to 56 participants — Native Spanish speakers — Confirmed diagnosis of dyslexia — Ages ranging from 11 to 56 (average around 20 - 21 years depending on the experiment) — Participants with attention deficit disorder — Frequent users of Internet and frequent readers — Education
— Same number — Idem !— Mapped !!!!— Similar — Similar
!— Tobii T50 (17-inch TFT monitor)
Eye-Tracker
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineMethodology — Materials
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Text Presentation — Controlled
Comprehension Questionnaires
— Multiple choice tests — Literal and inferential questions. — Correct, partially correct and wrong answers
1 2 3 4 5
muy fácil‘very easy’
muy difícil‘very difficult’
Facilidad comprensión ‘Ease of understanding’Subjective Ratings
Base Texts
— Same genre — Similar topics — Same number of sentences — Same number of words — Similar average word length — Same number of unique named entities, foreign words and same number/ type of numerical expressions
+ Text modifications (Independent variables)
Facilidad de Comprensión
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
— within-subjects design — between-subject design
Survey
Methodology — Design
Qualitative Data
Quantitative Data
Design
Dependent Variables
Statistical Tests
(conditions in counterbalanced order)
Likert scales
Eye tracking
Questionnaires
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineOutline
— What? !— Why?
— Goal !— Motivation — Understanding
— Text Presentation
— Text Content
— Applications— How?
— Methodology
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Understanding
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Which problems dyslexic people experience?
Are there linguistic foundations?
Linguistics
Cognitive Neuroscience
Natural Language Processing
How NLP could help dyslexic people?
How text presentation could help people with dyslexia?
Human Computer Interaction
Eye-trackingHow can we measure the reading performance?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Which problems dyslexic people experience?
Are there linguistic foundations?
Linguistics
Cognitive Neuroscience
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineWhy Errors?
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content Integration
!Dyslexia — Studying dyslexia — Diagnosing dyslexia — Accessibility tools !!The Web — Detecting spam — Measuring quality
Source of Knowledge
Errors
[Treiman, 1997] [Lindgrén & Laine, 2011]
[Schulte-Körne et al. 1996]
[Pedler, 2007]
[Piskorski et al. 2008]
[Gelman & Barletta, 2008]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineDyslexia in the Web
[Rello & Baeza-Yates, New Review of Hypermedia and Multimedia, 2012]
English Spanish
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineAre there Linguistic Foundations?
Written Errors by People with Dyslexia[Rello & Llisterri, LDW 1012 ]
[Rello, Baeza-Yates & Llisterri, LREC 2014]
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Analysis
Visual & Phonetic
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Please read this text. It is just an example but helps to underztand how we read text. A text can be
legivle but this does not mean that it will be compreensible. Hence, we habe to take care about
the presantation of a text as well as the lexical, syntactic, and semmantical levels of its content.
How Do We Process Text?
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Test
Outline
Demographic Questionnaire
Writing/memory test
Variant B
Comprehension Test
Comprehension Test
Comprehension Test
Comprehension Test
Variant A
Text 1: 16% errors Text 2: 16% errors
Text 2: 16% errors Text 1: 16% errors
Error Perception Test
Error Perception Test
— 0 or 12/75 words (16% errors) — dyslexic — unique
Errors
priosridad presupuetsos indutricas implse
[Rello & Baeza-Yates, WWW 2012 (poster)]
Does Lexical Quality Matters?
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Error Awareness Dependent Measure
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Lexical Quality
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
ρ = 0.799 (p < 0.001)
Group D no effects! Group N (p = 0.08)
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
[Rello & Baeza-Yates, WWW 2012 (poster)]
OutlineHow Fast You Can Read This?
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Olny srmat poelpe can raed tihs !!I cdnuolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg. Due to the phaonmneal pweor of the hmuan mnid, aoccdrnig to a raerscheer at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, t he olny iprmoatnt tihng is taht the frist and lsat ltteer are in the rgh it pclae. The ruslet can be a taotl mses but you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Amzanig huh? Yaeh and I awlyas tghuhot taht slpeling was ipmorantt!
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineHow Well We Process Text?
[Baeza-Yates & Rello, to be submitted, 2014]
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
How important is the order in our internal representation of words?
Words with Errors
50.0
62.5
75.0
87.5
100.0
No errors 8% errors 16% errors 50% errors
Without DyslexiaWith Dyslexia
Comprehension Score (%)
Reading Time also increases
Words with Errors
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineDo They See the Errors?
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
https://www.youtube.com/watch?v=P1dRqpRi4csSee VIDEO here:
OutlineContributions
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content Integration
— The presence of errors written by people with dyslexia in the text does not impact the reading performance of people with dyslexia, while it does for people without dyslexia.
— Normal –correctly written– texts present more difficulties for people with dyslexia than for people without dyslexia. To the contrary, texts with jumbled letters present similarly difficulties, for both, people with and without dyslexia.
— Lexical quality is a good indicator for text readability and comprehensibility, except for people with dyslexia.
— Written errors by people with dyslexia are phonetically and visually motivated. The most frequent errors involve the letter without a one-to-one correspondence between grapheme and phone. Most of the substitution errors share phonetic features and the letters tend to have certain visual features, such as mirror and rotation features.
— The rate of dyslexic errors is independent from the rate of spelling errors in web pages. Around 0.67% and 0.43% of the errors in the Web are dyslexic errors for English and Spanish, respectively. These rates are smaller than expected probably due to spelling correction aids.
Rello L., Baeza-Yates R., and Llisterri, J. DysList: An Annotated Resource of Dyslexic Errors. In: Proc. LREC’14. Reykjavik, Ice- land; 2014. p. 26–31.
Rello L., and Llisterri, J. There are Phonetic Patterns in Vowel Substitution Errors in Texts Written by Persons with Dyslexia. In: 21st Annual World Congress on Learning Disabilities (LDW 2012). Oviedo, Spain; 2012. p. 327–338
Rello L., and Baeza-Yates R. The Presence of English and Spanish Dyslexia in the Web. New Review of Hypermedia and Multimedia. 2012;8. p. 131–158
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Text Presentation
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
How text presentation could help people with dyslexia?
Human Computer Interaction
Which problems dyslexic people experience?
Are there linguistic foundations?
Linguistics
Cognitive Neuroscience
Natural Language Processing
How NLP could help dyslexic people?
Eye-trackingHow can we measure the reading performance?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
How text presentation could help people with dyslexia?
Human Computer Interaction
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineConditions Studied
— Font type
— Font size
— Font grey scale & background grey scale
— Color pairs
— Character spacing
— Line spacing
— Paragraph spacing
— Column width
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Text Presentation
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineWhy Fonts?
Fonts Designed for Dyslexia
User Studies
What is missing?
!Evidence via quantitative data !!!Participants !!!More fonts Most frequent fonts
RecommendationsThe British Dyslexia Association
sans-seriffonts
— Arial — no italics — no fancy fonts
Sylexiad, OpenDyslexic, Dyslexie & Read Regular
— Arial and Dyslexie — word-reading test — 21 students
[De Leeuw, 2010]
[Rello & Baeza-Yates, ASSETS 2013]
What has been done so far?
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineMethodology — Design
Italics roman !italic
Serif sans serif !serif
Spacing monospace !proportional
Independent variables
[Rello & Baeza-Yates, ASSETS 2013]
Understanding
Text Presentation Text Content Integration
Dyslexic specially designed !not specially designed
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Methodology — Design
[Rello & Baeza-Yates, ASSETS 2013]
Font ExperimentDesign Within-subjects
Independent Font Type ArialVariables Arial Italic
Computer Modern Unicode (CMU)CourierGaramondHelveticaMyriadOpenDyslexicOpenDyslexic ItalicTimesTimes ItalicVerdana
[±Italic] [�Italic][+Italic]
[± Serif] [�Serif][+Serif]
[±Monospace] [�Monospace][+Monospace]
[±Dyslexic] [� Dyslexic][+ Dyslexic]
[±Dyslexic It.] [� Dyslexic It.][+ Dyslexic It.]
Dependent Reading Time (objective readability)Variables Fixation Duration
Preference Rating (subjective preferences)Control Variable Comprehension Score (objective comprehensibility)
Participants Group D (48 participants) 22 female, 26 maleAge: range from 11 to 50(x̄ = 20.96, s = 9.98)Education: high school (26),university (19),no higher education (3)
Group N (49 participants) (28 female, 21 male)age range from 11 to 54(x̄ = 29.20, s = 9.03)Education: high school (17),university (27),no higher education (5)
Materials Texts 12 story beginningsText PresentationComprehension Quest. 12 literal items (1 item/text)Preferences Quest. 12 items (1 item/condition)
Equipment Eye tracker Tobii 1750
Procedure Steps: Instructions, demographic questionnaire,reading task (⇥ 12), comprehension questionnaire (⇥ 12),preferences questionnaire (⇥ 12)
Table 9.2: Methodological summary for the Font Experiment.
154
Font ExperimentDesign Within-subjects
Independent Font Type ArialVariables Arial Italic
Computer Modern Unicode (CMU)CourierGaramondHelveticaMyriadOpenDyslexicOpenDyslexic ItalicTimesTimes ItalicVerdana
[±Italic] [�Italic][+Italic]
[± Serif] [�Serif][+Serif]
[±Monospace] [�Monospace][+Monospace]
[±Dyslexic] [� Dyslexic][+ Dyslexic]
[±Dyslexic It.] [� Dyslexic It.][+ Dyslexic It.]
Dependent Reading Time (objective readability)Variables Fixation Duration
Preference Rating (subjective preferences)Control Variable Comprehension Score (objective comprehensibility)
Participants Group D (48 participants) 22 female, 26 maleAge: range from 11 to 50(x̄ = 20.96, s = 9.98)Education: high school (26),university (19),no higher education (3)
Group N (49 participants) (28 female, 21 male)age range from 11 to 54(x̄ = 29.20, s = 9.03)Education: high school (17),university (27),no higher education (5)
Materials Texts 12 story beginningsText PresentationComprehension Quest. 12 literal items (1 item/text)Preferences Quest. 12 items (1 item/condition)
Equipment Eye tracker Tobii 1750
Procedure Steps: Instructions, demographic questionnaire,reading task (⇥ 12), comprehension questionnaire (⇥ 12),preferences questionnaire (⇥ 12)
Table 9.2: Methodological summary for the Font Experiment.
154
Base Texts — comparable— Same genre — Same discourse structure — Same number of sentences: 11 — Same number of words: 60 — Similar word length (from 4.92 to 5.87 letters) — No acronyms, foreign words, or numerical expressions
— 12 different texts — 12 different fonts (counter-balanced)
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Fixation Duration
Fixation Duration: χ2 (11) = 93.63, p < 0.001D group
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Fixation Duration
Fixation Duration: χ2 (11) = 93.63, p < 0.001D group
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Fixation Duration
Fixation Duration: χ2 (11) = 93.63, p < 0.001D group
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Fixation Duration
Fixation Duration: χ2 (11) = 93.63, p < 0.001D group
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults
Partial order obtained from Reading Time and Preference Ratings
D group[Rello & Baeza-Yates, ASSETS 2013]
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
— Font types have an impact on readability of people (with/out dyslexia) !— OpenDys and OpenDys It. did not lead to a better or worse read !
Values with positive e↵ects forCondition Measures with Dyslexia without Dyslexia
Font Type Obj. Readability Arial ArialCourier CourierCMU CMUHelvetica Verdana
Preferences Verdana VerdanaHelvetica HelveticaArial Arial
Recommendation: Arial, Courier, CMU, Helvetica,and Verdana.
Font Face Obj. Readability roman romansans serif sans serifmonospaced monospaced
Preferences roman romansans serif no e↵ectsno e↵ects proportional
Recommendation: roman, sans serif and monospaced.
Font Size Obj. Readability 18, 22 and 18, 22 and26 points 26 points
Obj. Comprehensibility 18, 22 and 14, 18, 22 and26 points 26 points
Subj. Readability 18 and 22 points 18 and 22 pointsSubj. Comprehensibility 18, 22 and 14, 18, 22 and
26 points 26 pointsRecommendation: 18 and 22 points
Character Spacing Obj. Readability +7%, +14% +7%, +14%Preferences no e↵ects 0%Recommendation: ranging from 0 to +14%
Line Spacing Obj. Readability no e↵ects no e↵ectsObj. Comprehensibility 0.8, 1 and 1.2 lines no e↵ectsSubj. Readability no e↵ects no e↵ectsSubj. Comprehensibility no e↵ects 1 lineRecommendation: ranging from 1 to 1.5 lines
Paragraph Spacing Obj. Readability no e↵ects no e↵ectsPreferences no e↵ects no e↵ects
Grey Scale Obj. Readability no e↵ects no e↵ects(text) Preferences 0% 0%
Recommendation: 0% (black font)Grey Scale Obj. Readability no e↵ects no e↵ects(background) Preferences 0% 0%
Recommendation: 0% (black background)Color Obj. Readability no e↵ects no e↵ects(text/background) Preferences no e↵ects no e↵ects
Column Width Obj. Readability no e↵ects no e↵ectsPreferences no e↵ects 66 char./line
Table 16.1: Text presentation recommendations for more readable andunderstandable screen text for people with dyslexia.
289
[Rello & Baeza-Yates, ASSETS 2013]
Understanding
Text Presentation Text Content Integration
Results
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineText Presentation - Conditions
— Font type
— Font size
— Font grey scale & background grey scale
— Color pairs
— Character spacing
— Line spacing
— Paragraph spacing
— Column width
dyslexia
dyslexia
dyslexia
dyslexia dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
black/white
off-black/off-white
black/yellow
blue/white
dyslexia
dyslexia
dyslexia
d y s l e x i a
dyslexia
dyslexia
dyslexia
dyslexia
grey scale:0%
black/creme
dark brown/light mucky green
brown/mucky green
blue/yellow
char. spacing:+14%
+7%
0%
–7%
25%
50%
75%
dyslexia
dyslexia
dyslexia
dyslexiasize:14 p.
18 p.
22 p.
24 p.
dyslexia
dyslexia
dyslexia
dyslexia dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
dyslexia
black/white
off-black/off-white
black/yellow
blue/white
dyslexia
dyslexia
dyslexia
d y s l e x i a
dyslexia
dyslexia
dyslexia
dyslexia
grey scale:0%
black/creme
dark brown/light mucky green
brown/mucky green
blue/yellow
char. spacing:+14%
+7%
0%
–7%
25%
50%
75%
dyslexia
dyslexia
dyslexia
dyslexiasize:14 p.
18 p.
22 p.
24 p.
[Rello, Kanvinde & Baeza-Yates, W4A 2012]
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineText Presentation — Web
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
[Rello, Pielot, Marcos & Carlini, W4A 2013]
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineContributions
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
— Larger font sizes improve the readability, especially for people with dyslexia.
— Larger character spacing improve readability for people with and without dyslexia.
— For reading web text, font size of 18 points ensures good subjective and objective readability and comprehensibility.
— Sans serif, monospaced, and roman font types increase the readability of people with and without dyslexia, while italic fonts decrease it.
— Good fonts for people with dyslexia are Helvetica, Courier, Arial, Verdana and CMU, taking into consideration both, reading performance and subjective preferences.
Rello, L. and Baeza-Yates, R. Good Fonts for Dyslexia. Proc. ASSETS’13. Bellevue, Washington, USA: ACM Press; 2013.
Rello & Baeza-Yates, How to Present more Readable Text for People with Dyslexia. An eye-tracking study on text colors, size and spacings. To appear in Universal Access in the Information Society (UAIS).
Rello, L., Kanvinde, G., Baeza-Yates, R. Layout guidelines for web text and a web service to improve accessibility for dyslexics. In: Proc. W4A 2012. Lyon, France: ACM Press; 2012.
Rello L., Pielot M., Marcos, MC., and Carlini R. Size Matters (Spacing not): 18 Points for a Dyslexic-friendly Wikipedia. In: Proc. W4A ’13. Rio de Janeiro, Brazil: ACM Press; 2013.
Understanding
Text Presentation Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Text Content
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Natural Language Processing
How NLP could help dyslexic people?
Which problems dyslexic people experience?
Are there linguistic foundations?
Linguistics
Cognitive Neuroscience
How text presentation could help people with dyslexia?
Human Computer Interaction
Eye-trackingHow can we measure the reading performance?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
how?A Multidisciplinary Challenge
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Natural Language Processing
How NLP could help dyslexic people?
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineProblems of Dyslexia
Surface Dyslexia
— Less frequent words: prístino — Long words: colecciones — Substitutions of functional words: para, por — Confusions of small words: en, el, es
Phonology — Irregular words: vase — Homophonic words or pseudo homophonic words !— Foreign words
Discourse — Long sentences — Long paragraphs
Orthography — Orthographically similar words: homo, horno — Alternation of different typographical cases: ElefANte
Morphology — Derivational errors: *inmacularidad
Phonological Dyslexia
Lexicon & Syntax — New words: chocaviar — Pseudo–words and non–words: maledo
Cognitive Neuroscience
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineHow NLP can Help?
Difficulties
Orthography & Phonology
Derivational errors New words Pseudo-words Less frequent words Long words Functional words Small words
Morphology, Lexicon & Syntax
Strong visual thinkers Pattern Recognition
Visual Thinking
NLP
Orthographically similar Misspellings Irregular words Homophonic words Pseudo-homophonic words Foreign words
Strengths
Orthographic and Phonetic Similarity Measures Corpus Analyses
Lexical Simplification !Syntactic Simplification
— Word frequency — Word length — Numerical Representation — Paraphrases
Discourse Simplification
Long sentences Long paragraphs
Discourse — Graphical Schemes — Keywords
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Content Conditions
Understanding Text Presentation
Text Content Integration
— Errors
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Methodology — Design
[+LONG] [−LONG]
prestidigitador (3.75 shorter) !mago
[+FREQUENT] [−FREQUENT]
ataques (474 times more freq.)!!refriegas
Word Frequency and Word Length ExperimentsDesign within-subjects
Word Frequency ExperimentIndependent [±Frequent] [+Frequent]Variables [�Frequent]
Word Length Experiment[±Long] [+Long]
[�Long]
Dependent Reading Time (Objective readability)Variables (Sec. 3.1.1) Fixation Duration
Comprehension Score (Objective comprehensibility)
Participants Group D (23 participants) 12 female, 11 maleAge: range from 13 to 37(x̄ = 20.74, s = 8.18)Education: high school (11),university (10),no higher education (2)Reading: more than 8 hours (13.0%),4-8 hours (39.1%),less than 4 hours/day (47.8%)
Group N (23 participants) (13 female, 10 male)Age: range from 13 to 35(x̄ = 20.91, s = 7.33)Education: high school (6),university (16),no higher education (1)Reading: more than 8 hours (4.3%),4-8 hours (52.2%),less than 4 hours/day (43.5%)
Materials Texts 4 texts (2 texts/experiment)Synonym Pairs 15 in Word Frequency Exp.
6 in Word Length Exp.Text PresentationCompren. Quest. 8 inferential items (2 items/text)
Equipment Eye tracker Tobii 1750
Procedure Steps: (per experiment) Instructions, demographic questionnaire,reading task (⇥ 2), comprehension questionnaire (⇥ 2), andpreferences questionnaire (⇥ 2)
Table 10.2: Methodological summary for the Word Frequency andWord Length experiments.
the participants please refer to Section 3.1.2.
10.3.3 Materials
To study the e↵ects of word length and frequency, we need to study targetwords in context, that is, as part of a text. The rationale behind this is that
186
Target Words
— common names — non ambiguous names — no compound nouns — no foreign words — no homophonic words
Base Texts — comparable
Frequency— relative frequencies (one order of magnitude) — no short words
Length— at least double the length — longest words
Comprehension Questionnaires
— inferential questions
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Word-frequency
0.1 0.15 0.2 0.25 0.3 0.35 0.410
20
30
40
50
60
70
80
90
Mean fixation duration (s)
Vis
it d
ura
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s)
−freq +dys
+freq +dys
−freq −dys
+freq −dys
Fixation duration (sec.)
Readab
ility a
xis
Rea
ding
Tim
e (s
ec.)
0.1 0.15 0.2 0.25 0.3 0.35 0.4
90
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70
60
50
40
30
20
10Group N: [+Frequent] [–Frequent] Group D: [+Frequent] [–Frequent]
0.1 0.15 0.2 0.25 0.3 0.35 0.410
20
30
40
50
60
70
80
90
Mean fixation duration (s)
Vis
it d
ura
tion
(s)
−freq +dys
+freq +dys
−freq −dys
+freq −dys
0.1 0.15 0.2 0.25 0.3 0.35 0.410
20
30
40
50
60
70
80
90
Mean fixation duration (s)
Vis
it d
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tion
(s)
−freq +dys
+freq +dys
−freq −dys
+freq −dys
0.1 0.15 0.2 0.25 0.3 0.35 0.410
20
30
40
50
60
70
80
90
Mean fixation duration (s)
Vis
it d
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tion
(s)
−freq +dys
+freq +dys
−freq −dys
+freq −dys
0.1 0.15 0.2 0.25 0.3 0.35 0.410
20
30
40
50
60
70
80
90
Mean fixation duration (s)
Vis
it d
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(s)
−freq +dys
+freq +dys
−freq −dys
+freq −dys
— A larger number of high frequency words increases readability for people with dyslexia. !Reading Time t(33.488)=−2.120, p=0.035 Fixation Duration t(35.741)=−2.150, p=0.038
— No effects for Group N
[Rello, Baeza-Yates, Dempere & Saggion, INTERACT 2013]
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Results — Word-length
— The presence of short words compared to long words increases comprehensibility for people with dyslexia. !Comprehension Score t(38.636) = −2.396, p = 0.022 !— No effects for Group N
[Rello, Baeza-Yates, Dempere & Saggion, INTERACT 2013]
Understanding Text Presentation
Text Content Integration
— A total dissociation of frequency and length is not possible — Word frequency and word length are naturally related in language [Jurafsky et al., 2001]
Limitations
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Next Steps?Understanding Text Presentation
Text Content Integration
Implement and evaluate a lexical simplification algorithm
Find out how to make lexical simplification useful
Lexical Simplification
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineWhat has Been Done so far?
Experimental psychology and word processing
Accessibility studies about people with dyslexia
What is missing?
Spanish Word length Interaction strategies !!!Automatic !!
Natural language processing and lexical simplification
detect — complex words (Frequency)
substitute— dictionaries — Wordnet — ontologies
Frequent & long words
Content
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
Understanding Text Presentation
Text Content Integration
Design
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline Evaluation of Simplification Strategies
Independent variable (counter-balanced order)
Lexical simplification
ORIGINAL SUBSBEST SHOWSYNS GOLD
laptop iPad Android device
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
— Same genre: Scientific American — Similar topics: reports from Nature !— Same discourse structure !!!!— Same number of sentences: 11 — Same number of words: 302 — No acronyms nor numbers
Outline
Survey
Methodology — Design
Lexical Simplification Experiment.Design Within-subjects
Independent Lexical Simplification [Orig]Variables Strategy [SubsBest]
[ShowSyns][Gold]
Dependent Reading Time (objective readability)Variables Fixation Duration
Comprehension Score (objective comprehensibility)Subject. Readability Rating (subjective readability)Subject. Comprehension Rating (subjective comprehensibility)Subject. Memorability Rating (subjective memorability)
Participants Group D (47 participants) 28 female, 19 maleAge: range from 13 to 50(x̄ = 24.36, s = 10.19)Education: high school (18),university (26), no higher education (3)
Group N (49 participants) (29 female, 20 male)Age: range from 13 to 40(x̄ = 28.24, s = 7.24)Education: high school (16),university (31), no higher education (2)
Materials Base Texts 2 textsWord Substitutions 34 per text (in [SubsBest]), and
40/44 per text (in [Gold])Synonyms on-demand 100/110 synonyms for 50/55 words
per text (in [ShowSyns])Text PresentationComprehension Quest. 6 inferential items (3 per text)Sub. Readability Quest. 2 likert scales (1/condition level)Sub. Comprehension Quest. 2 likert scales (1/condition level)Sub. Memorability Quest. 2 likert scales (1/condition level)
Equipment Eye tracker Tobii 1750, Samsung Galaxy Ace S5830iPad 2, and MacBook Air
Procedure Steps: Instructions, demographic questionnaire, text choosing, readingtask, comprehension questionnaires, sub. readability quest.sub. comprehension quest., and subjective memorability quest.
Table 14.2: Methodological summary for the Keywords Experiment.
was not available. Then, we could not record the readings for this condition.Hence, for ShowSyns we implemented mock-ups on three di↵erent devices:smartphone, tablet, and laptop. In this way we made sure that our measureswere device independent. To cancel out possible e↵ects of a device, werotated the use of the devices amongst participants.
256
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
1&2p — Intro 3p — Background 4p — Details
Target Words
Base Texts
Engagement Choose the text you like!
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Objective Measures
r = 0.625r = 0.994 r = 0.429
Group D Group N
No effects!
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults — Subjective Measures
Subject. Readability
Subject. Comprehension
H(3) = 9.595, p = 0.022 [SubsBest] more difficult than [Original] (p = 0.003) and [ShowSyns] (p = 0.047)
H(3) = 9.020, p = 0.029 [SubsBest] significantly more difficult than [Gold] (p = 0.003)
Group D Group NSubject. Comprehension
Subject. Memorability
●
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●
Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original
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s) ●
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Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original
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Remember Group D Group N3.294118 3.888889 Original 0.15975821093.588235 3.700000 LexSIS4.142857 4.142857 Dyswebxia3.437500 4.375000 Gold
Comprehension
Group D Group N
3.235294 4.444444 Original -0.0849246333.647059 3.800000 LexSIS4.357143 4.285714 Dyswebxia3.750000 4.250000 Gold
Readability Group D Group N3.647059 4.222222 Original 0.24109926283.882353 3.900000 LexSIS4.285714 4.357143 Dyswebxia3.625000 4.250000 Gold
Reading Time Group D Group NOriginal 134.7920 90.24000LexSIS 135.7656 105.77059Gold 125.8575 89.07875
0.62505035990.99439359
0.45885163620.9277034419
Fixation Duration Group D Group NOriginal 0.2426667 0.2035714LexSIS 0.2418750 0.2035294Gold 0.2362500 0.1986667
3.647059 4.2222223.882353 3.900000
Comprehension Group D Group N 3.625000 4.250000Original 57.00000 63.88889LexSIS 50.00000 50.83333 0.6367350009 -0.999958492Dyswebxia 61.90476 63.09524 0.4685167431 -0.554366967Gold 50.19149 65.39130
0.42898981
3.294118 3.888889 Original3.588235 3.700000 LexSIS4.142857 4.142857 Dyswebxia3.437500 4.375000 Gold
80
97.5
115
132.5
150
[Original] [SubsBest] [Gold]
Reading Time(sec.)
0.18
0.198
0.215
0.233
0.25
[Original] [SubsBest] [Gold]
Fixation Duration(sec.)
1 2 3 4 5
Readability
Group D Group N
1 2 3 4 5
Understandability
Group D Group N
(ave.) (ave.)
Very bad Very good Very bad Very good
[Original]
[SubsBest]
[ShowSyns]
[Gold]
1 2 3 4 5
Memorability
Group D Group NVery bad Very good
(ave.)[Original]
[SubsBest]
[ShowSyns]
[Gold]
[Original]
[SubsBest]
[ShowSyns]
[Gold]
40
47.5
55
62.5
70
[Original] [SubsBest] [ShowSyns] [Gold]
Comprehension
Group D Group N
(%)
●
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●
Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original
0.10
0.15
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40
47.5
55
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[Original] [SubsBest] [ShowSyns] [Gold]
Comprehension
Group D Group N
Reading Time
Group D
[Original] [SubsBest] [Gold]
Fixation Duration
50
100
1
50
200
2
50
300
(sec
.)
0.1
0 0
.15
0.
20
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30
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ec.)
[Original] [SubsBest] [Gold] Group D Group N
(%)
Group D Group N
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s)
H(3) = 8.275, p = 0.041 [ShowSyns] easier than [Gold] (p = 0.034) and [Original] (p = 0.034)
H(3) = 12.197, p = 0.007 [ShowSyns] easier than [SubsBest] (p = 0.013) and [Original] (p = 0.001)
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineResults
[Rello, Baeza-Yates, Bott & Saggion, W4A 2013 (best paper award)]
Lexical Simplification
substitution negatively affects the reading experience
does not help objective
readability comprehension
subjective measures
interaction matters
showing synonyms on-demand makes texts more comprehensible and more readable
help to get out of the vicious circle
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Next Steps?
implement and evaluate a lexical simplification algorithm
via synonyms on demand is helpful
Lexical Simplification
language resource of synonyms available to be used in tools
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
What is missing?Resources for Lexical Simplification in Spanish
What has Been Done so far?
resource containing lists of synonyms ranked by their complexity
— no Simple Wikipedia in Spanish !— Simplext Corpus (200 news articles) 6,595 words original and 3,912 words simplified !— Spanish OpenThesaurus (SpOT) 21,378 target words (lemmas), 44,348 different word senses !— EuroWordNet 50,526 word meanings, 23,370 synsets
Understanding Text Presentation
Text Content Integration
[Baeza-Yates, Rello & Dembowski, to be submitted]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
— Google Books N-gram Corpus (5-grams) in Spanish (8,116,746 books, over 6% of all books, 83,967,471,303 tokens
Output:
Dyslexia Features
— Analysis of Corpus of dyslexic errors
+
CASSA
Simpler Synonyms Ranking
Relative Web Frequency
— CASSA ResourceInput:
Word Candidates
Relative Web Frequency
Filters
— Valid words — Proper names — Stop words
+Lemmatization
Complexity Detection
— List of Senses (from Spanish OpenThesaurus)— Web Frequencies
Context Frequency
Word SenseDisambiguation
— List of Senses — Google Books n-gram Corpus Context Frequencies
Understanding Text Presentation
Text Content Integration
[Baeza-Yates, Rello & Dembowski, to be submitted]
Context Aware Synonym Simplification Algorithm
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
CASSA Synonyms Resource for Spanish
CASSA disambiguated
CASSA baseline (Frequency)
Understanding Text Presentation
Text Content Integration
[Baeza-Yates, Rello & Dembowski, to be submitted]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Methodology — Design
[Rello & Baeza-Yates, W4A 2014 (best paper award runner-up)]
Understanding Text Presentation
Text Content Integration
Evaluation Dataset— 80 target words
HIGH freq.
LOW freq.— Contexts and sentences (20th, 21st Century books)
vs. 130 [Biran et al. 2011] and 200 [Yatskar et al. 2010]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Survey
Results — Synonymy & Simplicity
— Ratings of Group N significantly higher than Group G for all the conditions !— Low frequency: better results for all ratings and conditions !— CASSA: More accurate and simpler synonyms Synonymy Rating (groups D & N) (H(1) = 110.36, p < 0.001), (H(1) = 198.72, p < 0.001) Simplicity Rating (groups D & N) (H(1) = 131.76, p < 0.001), (H(1) = 179.82, p < 0.001)
— Test well calibrated:expected low value answers: 1.41 (s = 0.98) for group D, 1.47 (s = 0.51) for Group N expected high value answers: 8.77 (s = 0.93) for group D, 9.16 (s = 0.69) for Group N
[Rello & Baeza-Yates, W4A 2014 (best paper award runner-up)]
Understanding Text Presentation
Text Content Integration
— New algorithm CASSA, outperforms the hard-to-beat Frequency Baseline [Specia et al. 2012]
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
— Word frequency
— Word length
— Numerical Representation
— Paraphrases
— Graphical Schemes
— Keywords
Conditions Studied
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Text Content
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineContributions
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
— Frequent words improve readability while shorter words may improve comprehensibility, especially in people with dyslexia.
— Numbers represented as digits instead of words, as well as percentages instead of fractions, improve readability of people with dyslexia.
— Graphical schemes improve the subjective readability and comprehensibility of people with dyslexia.
— Highlighted keywords increases the objective comprehension by people with dyslexia, but not the readability.
— Lexical simplification via automatic substitution of complex words by simpler synonyms is not helpful. However, showing synonyms on demand improves the subjective readability and comprehensibility of people with dyslexia.
Rello, L., Baeza-Yates, R., Dempere, L. and Saggion, H. Frequent Words Improve Readability and Short Words Improve Understand- ability for People with Dyslexia. Proc. INTERACT ’13. Cape Town, South Africa: IFIP Press; 2013, p. 203–219
Rello, L., Bautista, S., Baeza-Yates, R., Gervás, P., Hervás, R. and Saggion, H. One Half or 50%? An Eye-Tracking Study of Number Representation Readability. Proc. INTERACT ’13. Cape Town, South Africa: IFIP Press; 2013, p. 229-245
Rello, L., Baeza-Yates, R., Bott, S. and Saggion, H. Simplify or Help? Text Simplification Strategies for People with Dyslexia. Proc. W4A ’13. Rio de Janeiro, Brazil: ACM Press; 2013 (best paper award).
Rello, L. and Baeza-Yates, R. Evaluation of DysWebxia: A Reading App Designed for People with Dyslexia. Proc. W4A ’14. Seoul, South Korea: ACM Press; 2014 (Chapter 15 [319], best paper nominee).
Understanding Text Presentation
Text Content Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
Integrating Form and Content
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Values with positive e↵ects forCondition Measures with Dyslexia without Dyslexia
Font Type Obj. Readability Arial ArialCourier CourierCMU CMUHelvetica Verdana
Preferences Verdana VerdanaHelvetica HelveticaArial Arial
Recommendation: Arial, Courier, CMU, Helvetica,and Verdana.
Font Face Obj. Readability roman romansans serif sans serifmonospaced monospaced
Preferences roman romansans serif no e↵ectsno e↵ects proportional
Recommendation: roman, sans serif and monospaced.
Font Size Obj. Readability 18, 22 and 18, 22 and26 points 26 points
Obj. Comprehensibility 18, 22 and 14, 18, 22 and26 points 26 points
Subj. Readability 18 and 22 points 18 and 22 pointsSubj. Comprehensibility 18, 22 and 14, 18, 22 and
26 points 26 pointsRecommendation: 18 and 22 points
Character Spacing Obj. Readability +7%, +14% +7%, +14%Preferences no e↵ects 0%Recommendation: ranging from 0 to +14%
Line Spacing Obj. Readability no e↵ects no e↵ectsObj. Comprehensibility 0.8, 1 and 1.2 lines no e↵ectsSubj. Readability no e↵ects no e↵ectsSubj. Comprehensibility no e↵ects 1 lineRecommendation: ranging from 1 to 1.5 lines
Paragraph Spacing Obj. Readability no e↵ects no e↵ectsPreferences no e↵ects no e↵ects
Grey Scale Obj. Readability no e↵ects no e↵ects(text) Preferences 0% 0%
Recommendation: 0% (black font)Grey Scale Obj. Readability no e↵ects no e↵ects(background) Preferences 0% 0%
Recommendation: 0% (black background)Color Obj. Readability no e↵ects no e↵ects(text/background) Preferences no e↵ects no e↵ects
Column Width Obj. Readability no e↵ects no e↵ectsPreferences no e↵ects 66 char./line
Table 16.1: Text presentation recommendations for more readable andunderstandable screen text for people with dyslexia.
289
Text Presentation Recommendations
[Rello & Baeza-Yates, to appear in Universal Access in the Information Society (UAIS)]
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Outline
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Text Presentation Recommendations
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
[Rello & Baeza-Yates, to appear in Universal Access in the Information Society (UAIS)]
OutlineText Content
Recommendations
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
[Rello, Baeza-Yates, Dempere & Saggion, INTERACT 2013][Rello, Bautista, Baeza-Yates, Gervás, Hervás & Saggion, INTERACT 2013]
OutlineText Content
Recommendations
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
[Rello, Baeza-Yates & Saggion. CICLing 2013][Rello, Saggion & Baeza-Yates, PITR 2014]
[Rello, Baeza-Yates, Saggion & Graells, PITR 2012][Rello, Baeza-Yates, Bott, & Saggion, W4A 2013]
[Rello, L. and Baeza-Yates. W4A 2014]
how?Applications
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
IDEAL e-Book reader
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineIDEAL eBook Reader
[Kanvinde, Rello & Baeza-Yates, ASSETS 2012 (demo)]
— 35,000 downloads — Finalist - Vodafone Foundation Smart Accessibility Awards 2012 — Usability Evaluation - 14 participantsAccessible Systems
Mumbai, India
— Table of contents — Supports text-to-speech technology. — Spells word-by-word or letter-by-letter. — Write a comment.
Google Play
https://play.google.com/store/apps/details?id=org.easyaccess.epubreader
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
a) PDF without IDEAL eBook Reader
b) IDEAL eBook Reader, Dyslexia option
c) Word Spelling
d) Highlight Options e) Making Notes
a) PDF without IDEAL eBook Reader
b) IDEAL eBook Reader, Dyslexia option
c) Word Spelling
d) Highlight Options e) Making Notes
a) PDF without IDEAL eBook Reader
b) IDEAL eBook Reader, Dyslexia option
c) Word Spelling
d) Highlight Options e) Making Notes
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
‘Simpler’
Ideal
Configuration
Font
Synonyms
Color
Helvetica
Outline
[Rello, Baeza-Yates, Saggion, Bayarri & Barbosa, ASSETS 2013 (demo)]
iOS Reader
Soon in the App Store — Usability evaluation with 12 participants
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineText4all DysWebxia
[Rello, Baeza-Yates, Bott, Saggion, Carlini, Bayarri, Gorriz, Kanvinde, Gupta, Topac 2013 (challenge)] [Topac 2014 (PhD thesis)]
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
by Vasile Topac Polytechnic University of Timisoara, Romania
— Finalist in The Paciello Group Web Accessibility Challenge
http://www.text4all.net/dyswebxia.html
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Tools Overview
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content
Integration
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineOngoing Work
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Understanding Text Presentation Text Content
Integration
— Departament d’Ensenyament (Àrea de Tecnologies per a l'Aprenentatge i el Coneixement) Department of Education (Technologies for Learning) !!!— Cloud4All Project with Technosite !!— Web standards
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineMain Contributions
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
!— A new model called DysWebxia, that combines all our results and that has been integrated so far in four reading tools.
!!— Two new available language resources
http://www.luzrello.com/Resources
— Text Content Recommendations
— Text Presentation Recommendations
— DysList, a list of dyslexic errors annotated with linguistic, phonetic and visual features.
!— CASSA List, a new resource for Spanish lexical simplification composed of a list of disambiguated complex words, their context, and their corresponding simpler synonyms, ranked by complexity.
— Written errors — Processed differently (reading) by people with and without dyslexia
— Phonetically and visually motivated
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
OutlineAcknowledgments
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
Ricardo Baeza-Yates
Horacio Saggion
Gaurang Kanvinde
Vasile Topac
Joaquim Llisterri
Mari-Carmen Marcos
Laura Dempere
Simone Barbosa
Clara Bayarri
Stefan Bott
Roberto Carlini
Families with children with dyslexia
People with dyslexia
Yolanda Otal de la Torre
María Sanz-Pastor Moreno de Alborán
Luis Miret
Martin Pielot
Julia Dembowski
Eduardo Graells
Diego Saez-Trumper
Azuki Gorriz
Verónica Moreno
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
Thank you
How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013
luzrello@acm.org
PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona
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