EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical
  Properties to Aid in Generation of Technical Gestures
Sameena Hossain Payal Kamboj Aranyak Maity Tamiko Azuma Ayan Banerjee Sandeep K. S. Gupta
Gestures that share similarities in their forms and are related in their
meanings, should be easier for learners to recognize and incorporate into their
existing lexicon. In that regard, to be more readily accepted as standard by
the Deaf and Hard of Hearing community, technical gestures in American Sign
Language (ASL) will optimally share similar in forms with their lexical
neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical
relations within a set of technical gestures. We use automated identification
for 3 unique sub-lexical properties in ASL- location, handshape and movement.
EdGCon assigned an iconicity rating based on the lexical property similarities
of the new gesture with an existing set of technical gestures and the
relatedness of the meaning of the new technical word to that of the existing
set of technical words. We collected 30 ad hoc crowdsourced technical gestures
from different internet websites and tested them against 31 gestures from the
DeafTEC technical corpus. We found that EdGCon was able to correctly
auto-assign the iconicity ratings 80.76% of the time.