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Semantic Network Model for Sign Language Comprehension

Semantic Network Model for Sign Language Comprehension

Xinchen Kang, Dengfeng Yao, Minghu Jiang, Yunlong Huang, Fanshu Li
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 19
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781683180197|DOI: 10.4018/IJCINI.309991
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MLA

Kang, Xinchen, et al. "Semantic Network Model for Sign Language Comprehension." IJCINI vol.16, no.1 2022: pp.1-19. http://doi.org/10.4018/IJCINI.309991

APA

Kang, X., Yao, D., Jiang, M., Huang, Y., & Li, F. (2022). Semantic Network Model for Sign Language Comprehension. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 16(1), 1-19. http://doi.org/10.4018/IJCINI.309991

Chicago

Kang, Xinchen, et al. "Semantic Network Model for Sign Language Comprehension," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 16, no.1: 1-19. http://doi.org/10.4018/IJCINI.309991

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Abstract

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.