Quantifying Semantic Congruence to Aid in Technical Gesture Generation in Computing Education

Hossain, Sameena; Banerjee, Ayan; Gupta, Sandeep

doi:10.1007/978-3-031-11647-6_63

Cited by 1 publication

(2 citation statements)

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“…There have also been efforts to develop interconnected network of ASL gestures that are similar in form ( [4,9,15]) following the precedence of the network of interconnected English words based on their meanings and concepts [6,19]. In this section, we discuss the tools and concepts used in this paper.…”

Section: Preliminariesmentioning

confidence: 99%

“…The gesture learning mechanism was evaluated in [2], and automated feedback based on sublexical properties was evaluated in [10]. A different approach to finding semantic similarity was investigated in [9]. CSignGen framework is a part of Computer Science Accessible Virtual Education (CSAVE) platform, a personalized technical education platform for DHH individuals [8].…”

Section: Use Of Edgcon In Dhh Educationmentioning

confidence: 99%

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

Hossain

Kamboj

Maity

et al. 2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

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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

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