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Shuklin, D. E.

doi:10.1023/a:1013875126935

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Model of semantic networks (SNs) are generally used for processing natural languages (Shuklin, 2001). SNs, as knowledge representations, are extensible and have been used to model mental disturbances (Geva et al, 2000).…”

Section: Semantic Network Model (Snm)mentioning

confidence: 99%

“…Such direct matching also represents the most natural way of comprehending SLs in the brain. From this perspective, the authors present a computational cognitive model for SL comprehension that is based on the cognitive functionalities of the human brain combined with a knowledge representation theory of artificial intelligence (Shuklin, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Semantic Network Model for Sign Language Comprehension

Kang

Yao

Jiang

et al. 2022

International Journal of Cognitive Informatics and Natural Intelligence

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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 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.

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Section: Semantic Network Model (Snm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%