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.

翻译：在形式和意义上具有相似性的手势，有助于学习者识别并将其融入现有词汇体系。为此，为使技术手势更易被聋人和重听群体接受为标准手势，美国手语（ASL）中的技术手势应与其词汇邻居在形式上保持最优相似性。我们利用ASL词汇数据库ASL-LEX识别一组技术手势中的词汇关系。通过自动化方法识别ASL中三个独特的子词汇属性——位置、手形和运动。EdGCon根据新手势与现有技术手势在词汇属性上的相似性，以及新技术词汇与现有技术词汇在语义上的关联性，分配其象似性评级。我们从不同互联网网站收集了30个临时众包技术手势，并与来自DeafTEC技术语料库的31个手势进行对比测试。结果表明，EdGCon能够正确自动分配象似性评级，准确率达80.76%。