@inproceedings{b8bec63d3cbe4eb7bb056d63fb7add77,
title = "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 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.",
keywords = "ASL gestures, ASL lexicon, automated identification, technical gestures",
author = "Sameena Hossain and Payal Kamboj and Aranyak Maity and Tamiko Azuma and Ayan Banerjee and Sandeep Gupta",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 38th Annual ACM Symposium on Applied Computing, SAC 2023 ; Conference date: 27-03-2023 Through 31-03-2023",
year = "2023",
month = mar,
day = "27",
doi = "10.1145/3555776.3577623",
language = "English (US)",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery",
pages = "3--10",
booktitle = "Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023",
}