1 / 10

Customized Spell Corrector

Customized Spell Corrector. Aviad Ashkenazi Matan Zinger March 2012. Agenda. Overview about Dysgraphia Short overview of Natural Language Processing Using NLP to solve Dysgraphia symptoms Dispeller Application Demonstration. Overview – Cognitive Writing Process.

keren
Download Presentation

Customized Spell Corrector

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Customized Spell Corrector Aviad Ashkenazi Matan Zinger March 2012

  2. Agenda • Overview about Dysgraphia • Short overview of Natural Language Processing • Using NLP to solve Dysgraphia symptoms • Dispeller Application Demonstration

  3. Overview – Cognitive Writing Process • Dysgraphia may caused by a “damage” in any of this modules.

  4. Overview – Different Types of Dysgraphia • 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

  5. Overview – Natural Language Processing • 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

  6. NLP to Solve Dysgraphia Symptoms • 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)

  7. The DyspellerApplication • Classification Module • Use a series of tests (presented as a “game”) • Determines “Dysgraphia Profile” – common symptoms • 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. • Publishing Module • The corrected text can be sent via SMS or Email to any of the contacts.

  8. Dyspeller- Design Suggestion Processing – Calculating Dysgraphia Distance Phonetic Replace Symptom HTTP/GET: Suggestions by symptoms Double Letter Symptom Internal Reorder Symptom Valid Words Data Set Response: misspelled word -> suggestions list (JSON format)

  9. DEMO

  10. Thank You. References: • Gvion, Friedmann, Yachini – Dysgraphia (2008) • Letter position dysgraphia (AviahGvion, NaamaFriedmann) – 2009 • Dyscravia: Voicing substitution dysgraphia(AviahGvion, NaamaFriedmann) – 2010

More Related