Semantic Network Model for Sign Language Comprehension
This work addresses sign language processing for deaf and hard-of-hearing communities, but it is incremental as it builds on existing cognitive models.
The authors tackled the problem of sign language comprehension by proposing a semantic network model with a spreading activation search method, which improved performance for classifier predicates.
In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts, it is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g. concepts in the semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. The results demonstrate that the proposed search method improves the performance of sign language comprehension in the SNM.