Semantic Network Model for Sign Language Comprehension

In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on functionalities in human processing. The semantic network (SNM) that represents relations between concepts is used as form of knowledge representation. proposed applied classifier predicates. spreading activation search method initiated by labeling set source nodes (e.g., network) weights or “activation” then iteratively propagating “spreading” out to other linked nodes. results demonstrate improves performance SNM.