EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures

Abstract

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. 1

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI

“…If we assume consistency as true positive (TP), failure as FN and success in rejecting non-SOZ IC as true negative (TN), and failure to reject non-SOZ ICs as false positive (FP), then the results indicate significant FPs. Banerjee et al (17) Knowledge integration into DL models has been recently explored in many domains (28,29) including medical imaging (30) for diagnosis, lesion, or organ segmentation with great success rate. Expert knowledge can be integrated in two broad ways (28): (a) scientific knowledge, through mathematical models as performed in molecular dynamics analysis, or (b) experiential knowledge, through logic rules.…”

“…Knowledge integration into DL models has been recently explored in many domains ( 28 , 29 ) including medical imaging ( 30 ) for diagnosis, lesion, or organ segmentation with great success rate. Expert knowledge can be integrated in two broad ways ( 28 ): (a) scientific knowledge, through mathematical models as performed in molecular dynamics analysis, or (b) experiential knowledge, through logic rules.…”