Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with...

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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

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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.

Transcript of Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with...

Page 1: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 2: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

OutlineOutline

— What? !— Why?

— Goal !— Motivation — Understanding

— Text Presentation

— Text Content

— Integration— How?

— Methodology

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona

Applications

Page 3: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

OutlineMain Goal

Improve Digital Accessibility

People with Dyslexia

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona

Page 4: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 5: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 6: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 7: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 8: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 9: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 10: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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:

Page 11: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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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

Page 13: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 14: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

OutlineOutline

— What? !— Why?

— Goal !— Motivation — Understanding

— Text Presentation

— Text Content

— Applications— How?

— Methodology

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona

Page 15: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 16: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 17: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 18: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 19: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 20: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 21: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 22: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 23: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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)]

Page 24: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 25: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 26: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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:

Page 27: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 28: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 29: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 30: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 31: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 32: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 33: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 34: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 35: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 36: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 37: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 38: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 39: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 40: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 41: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 42: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 43: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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Outline

Text Content

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona

Page 45: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 46: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 47: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 48: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 49: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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Page 50: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

tion (

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

80

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

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

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

ura

tion

(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

Page 51: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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Page 52: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 53: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 54: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 55: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

—  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

Page 56: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 57: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

0.10

0.15

0.20

0.25

0.30

0.35

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s)

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

0.10

0.15

0.20

0.25

0.30

0.35

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s)

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

0.10

0.15

0.20

0.25

0.30

0.35

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s) ●

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

5010

015

020

025

030

0

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s) ●

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

5010

015

020

025

030

0

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s) ●

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

5010

015

020

025

030

0

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s)

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

(%)

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

0.10

0.15

0.20

0.25

0.30

0.35

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s)

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

5010

015

020

025

030

0

Font Size

Fixa

tion

Dura

tion

Mea

n (m

s)

40

47.5

55

62.5

70

[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

0.25

0.

30

0.3

5(s

ec.)

[Original] [SubsBest] [Gold] Group D Group N

(%)

Group D Group N

Dys.Gold Dys.lesSIS Dys.lexSIS Dys.Original

5010

015

020

025

030

0

Font Size

Fixa

tion

Dura

tion

Mea

n (m

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

Page 58: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 59: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 60: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 61: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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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

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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

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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

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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

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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

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Outline

Integrating Form and Content

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona

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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

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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)]

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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]

Page 71: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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]

Page 72: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

Page 73: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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‘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

Page 75: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

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

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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

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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

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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

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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

Page 80: Luz rello - Ph.D. Thesis presentation - DysWebxia: A Text Accessibility Model for People with Dyslexia

Thank you

How people with dyslexia read and what can HCI and NLP do about it? Keynote at DSAI 2013

[email protected]

PhD Thesis Defense — 27th June 2014, Universitat Pompeu Fabra, Barcelona