Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Building a phonics engine for automated text guidance

430 views

Published on

Presentation on the results of the iLearnRW project at IISA 2015 in Corfu, July 2015.

Published in: Education
  • Be the first to comment

  • Be the first to like this

Building a phonics engine for automated text guidance

  1. 1. Building a Phonics Engine for Automated Text Guidance Dominik Lukeš Dyslexia Action Chris Litsas NTUA www.ilearnrw.eu
  2. 2. Outline • Struggling readers needs • Linguistic background • Phonics engine need • Phonics engine specification • Phonics engine implementation • Phonics engine applications • Next steps
  3. 3. Needs of dyslexic people • Identifying the syllables in a word • Recognising the structure of words (stem, prefix, suffix) • Highlighting typical or repeated patterns of English orthography • Identifying phoneme/grapheme correspondence • Learning the pronunciation of a word • Learning the meaning of a word
  4. 4. Linguistic background Dearest creature in creation Studying English pronunciation, I will teach you in my verse Sounds like corpse, corps, horse and worse. Though the difference seems little, We say actual, but victual, Seat, sweat, chaste, caste, Leigh, eight, height, Put, nut, granite, and unite. Gerard Nolst Trenité - The Chaos (1922)
  5. 5. Linguistic background • tough, though, through, bough, thought, cough, hiccough • hosp.i.tal vs. hos.pit.al • kitt.en vs. kit.ten • walked, stopped, faked, tried • exgirlfriend vs. exigent vs. exit • English vs. Greek
  6. 6. Phonics engine need • Finding all examples of ‘a’ spelled to rhyme with ‘hay’ in a text or a corpus. • Sorting words by their phoneme/grapheme ratio. • Identifying appropriate syllable boundaries in the written form of a multi-syllable word based on knowledge of the syllable boundaries in pronunciation
  7. 7. Phonics engine specification • provide automated guidance to students and teachers reading texts (using highlighting as well as explicit information) • generate more extensive word lists for practice activities within the serious games • provide information about word structure to the game engine
  8. 8. Phonics engine implementation • Profile of phonic difficulties • Annotated phonic dictionary • Look up routines
  9. 9. Phonics profile - categories Based on a modified and expanded version of Dyslexia Action Literacy Programme • Consonants (49) • Vowels (71) • Blends and letter patterns (131) • Syllables (13) • Suffixes (92) • Prefixes (42) • Confusing letters (15)
  10. 10. Phonics profile (JSON) {"descriptions":["a-æ"], "problemType":"LETTER_EQUALS_PHONEM E", "humanReadableDescription":"a=æ (at) <> Pronounce a as æ. For example: at, as, and", "cluster":3, "character":"Short vowel"}
  11. 11. Phonic dictionary Word form: feelings Related stem: feeling Pronunciation: ˈfiː.lɪŋz Phoneme/Grapheme Mapping: f-f,ee-iː,l-l,i-ɪ,ng-ŋ,s-z Orthographic syllabification: fee.lings Number of letters: 8 Number of phonemes: 6 Number of syllables: 2 Frequency band: 4 Suffix type: SUFFIX_ADD Suffix form: s Prefix type: PREFIX_NONE Prefix form: NULL
  12. 12. Building the phonic dictionary • 5,000 most frequent words based on COCA • Generated derived forms by reversing hunspell • Used online tool to generate pronunciation • Create rules for matching pronunciation with spelling patterns • Create rules for displaying • Mark suffixes and prefixes and types • Adjust frequencies • Manual fine tuning (lots of regex)
  13. 13. Phonics engine applications • Phonics aware reader • Game support – generating word lists • Game support – provide word structure • Game support – link word structures to profile • Text classification tool • Online text annotation tool
  14. 14. Phonics aware reader
  15. 15. Phonics aware reader
  16. 16. Game support
  17. 17. Online text tools
  18. 18. Online text tools
  19. 19. Online text tools
  20. 20. Online text tools
  21. 21. Next steps • Bigger dictionary with more information on words • Finetuning of look up routines • More sophisticated highlighting routines • More sophisticated NLP – PoS – Sentence structure – Semantics • WordNet, Framenet • Named Entities • Collocations
  22. 22. www.ilearnrw.eu

×