Presentation

impulseverseAI and Robotics

Oct 24, 2013 (4 years and 16 days ago)

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Aviad

Ashkenazi

Matan

Zinger

March 2012


Overview about Dysgraphia


Short overview of Natural Language
Processing


Using NLP to solve Dysgraphia symptoms


Dispeller Application Demonstration



Dysgraphia

may caused by a “damage” in any of this modules.


Surface
Dysgraphia



damage in lexical flow


Using sub
-
lexical flow instead


Symptoms: replacing homo
-
phonetic letters, difficulty in
irregular words


No mistakes will appear for univalent

words


Similar symptoms will appear for children (w/o
dysgraphia
)


Phonological
Dysgraphia



damage in sub
-
lexical
flow


Difficulty in writing non
-
familiar words (which require
translation of phoneme into grapheme)


No mistakes when using lexical flow (e.g. for familiar words)


Peripheral
Dysgraphia



damage in grapheme buffer


Word length is one of the most critical factors


Symptoms: re
-
ordering of internal letters, doubling letters,
omitting letters




Purpose:

Machine’s understanding of human
-
generated text


Common terminology:


Tokenization


Lemmatization / Stemming


“Stop Words”


Part of Speech Tagging


Text Search, TF
-
IDF


Levenshtein

Distance


For spell checking / fuzzy search


Ranking by the level of distance


Semantic Understanding


Popular Open
-
Source Library: Lucene.NET


Provides many generic NLP capabilities


Regular spell checker


For which cases will it work well?


Is it good enough for Dysgraphia?


Customized spell checker


How will it work?


What is required?


Isn’t it better?



Symptoms we chose to handle


Homophonic replacement of letters (“
Dyscravia
”)


Doubling letters (Grapheme Buffer
Dysgraphia
)


Changing internal order (Grapheme Buffer
Dysgraphia
)

1.
Classification Module


Use a series of tests (presented as a “game”)


Determines “Dysgraphia Profile”


common symptoms

2.
Personalized Spell Checker


For every misspelled word, we look for the nearest correct word


Search is done not by
Levenshtein

distance, but by
“Personalized Dysgraphia Distance”


The distance between two words is calculated by:


Number of Dysgraphia symptoms which are typical for this
specific user,

that are needed to be fixed in order to
generate word A from word B.

3.
Publishing Module


The corrected text can be sent via SMS or Email to any of the
contacts.

Suggestion Processing


Calculating Dysgraphia Distance

Double
Letter
Symptom

Internal
Reorder
Symptom

Phonetic
Replace
Symptom

Valid
Words
Data Set

HTTP/GET:

Suggestions by symptoms

Response:

misspelled word
-
> suggestions list

(JSON format)

References:


Gvion
,
Friedmann
,
Yachini



Dysgraphia

(2008)


Letter position
dysgraphia

(
Aviah

Gvion
,
Naama

Friedmann
)


2009


Dyscravia
: Voicing substitution
dysgraphia

(
Aviah

Gvion
,
Naama

Friedmann
)


2010