[2301.11709] Semantic Network Model for Sign Language Comprehension
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Computer Science > Computation and Language

arXiv:2301.11709 (cs)
[Submitted on 27 Jan 2023]

Title:Semantic Network Model for Sign Language Comprehension

Authors:Xinchen Kang (1), Dengfeng Yao (1,2), Minghu Jiang (2), Yunlong Huang (2), Fanshu Li (1) ((1) Beijing Key Lab of Information Service Engineering, Beijing Union University, Beijing, China. (2) Lab of Computational Linguistics, Tsinghua University, Beijing, China.)
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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, 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.
Comments: 19 pages, 6 figures and 1 table
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2301.11709 [cs.CL]
  (or arXiv:2301.11709v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2301.11709
Journal reference: 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
Related DOI: https://doi.org/10.4018/IJCINI.309991

Submission history

From: Xinchen Kang [view email]
[v1] Fri, 27 Jan 2023 13:43:02 UTC (684 KB)
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