Zijun Shen, Hanjing Hu, Mingting Zhao, Minjie Lai, Kamran Zaib


Phonology, semantics, and pragmatics are the three aspects of language fundamental to know how language works and how people process and use it. By examining the interaction between the memory hypothesis and linguistics in speech and semantic organization, this study seeks to better understand how individuals use language to express themselves. This study delves into finding out the association between phonology and semantics by developing a multi-faceted correspondence organization and applying ideas from computer network science and chart hypothesis. A correspondence framework of the International Phonetic Alphabet and semantic network examination in R was used to investigate the information once obtained through a web-based information gathering drive. As per the discoveries, the dynamic and ensnared association between phonology and semantics affects the correspondence that happens in the media. The aftereffects of this study offer new points of view on the idea of language and the job it plays in human communication. Understanding how semantics and phonics interact in different situations is essential for modern communication, this study is an effort to analyze the relation between words' meanings and sounds. By studying words usage in different mediums, we want to learn how these important elements affect platform interactions. Thus, the present study explains how semantics and phonology shape media and how this link can improve communication. The review suggests a multi-faceted framework that divides the linguistic framework into various layers with different hub relationships. This advanced four-layer contact network is designed to identify and analyze language confusion with precision and accuracy. It incorporates free associations, word associations, and three semantic layers to measure articulation, semantics, punctuation, and jargon. Using a comprehensive inside-and-out technique, this evaluation examines phonology and semantics through multi-faceted network analysis, allowing for effective assessment of language understanding and improvement.


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Alamri, A. (2017). Phonological, Semantic and Root Activation in Spoken Word Recognition in Arabic: Evidence from Eye Movements (Doctoral dissertation, Université d'Ottawa/University of Ottawa).

Alamri, A. A., & Zamuner, T. S. (2015). Phonological, semantic, and root activation in spoken word recognition in Arabic: An eye-tracking study. In Proceedings of the annual conference of the Canadian Linguistics Association (pp. 1–15).

Avila Varela, D. S. (2022). Phonological and Semantic Overlap Between Words: Effects on Recognition of Familiar and Recently Acquired Words in Early Childhood.

Diaz, M. T., Johnson, M. A., Burke, D. M., Truong, T. K., & Madden, D. J. (2017). Age-related differences in the influence of task-irrelevant information on the neural bases of phonological and semantic processes. bioRxiv, 136606.

Dickens, J. V., Fama, M. E., DeMarco, A. T., Lacey, E. H., Friedman, R. B., & Turkeltaub, P. E. (2019). Localization of phonological and semantic contributions to reading. Journal of Neuroscience, 39(27), 5361-5368.

Edler, D., Bohlin, L., & Rosvall, M. (2017). Mapping higher-order network flows in memory and multilayer networks with infomap. Algorithms, 10(4), 112.

Elsisy, A., Mandviwalla, A., Szymanski, B. K., & Sharkey, T. (2022). A network generator for covert network structures. Information Sciences, pp. 584, 387–398.

Enarvi, S., Smit, P., Virpioja, S., & Kurimo, M. (2017). Automatic speech recognition with extensive conversational Finnish and Estonian vocabularies. IEEE/ACM Transactions on audio, speech, and language processing, 25(11), 2085-2097.

Fawcett, J. M., Bodner, G. E., Paulewicz, B., Rose, J., & Wakeham-Lewis, R. (2022). Production can enhance semantic encoding: Evidence from forced-choice recognition with homophone versus synonym lures. Psychonomic Bulletin & Review, 29(6), 2256-2263.

Fourtassi, A., Bian, Y., & Frank, M. C. (2020). The growth of children's semantic and phonological networks: Insight from 10 languages. Cognitive Science, 44(7), e12847.

Gafni, C., & Tsur, R. (2021). Studying emotive effects in poetry by quantifying open-ended impressions. Empirical Studies of the Arts, 39(2), 216-242.

Ha, H., Han, H., Mun, S., Bae, S., Lee, J., & Lee, K. (2019). An improved multilevel semantic network visualization study for analyzing sentiment words of movie review data. Applied Sciences, 9(12), 2419.

Han, G., Liu, L., Zhang, W., & Chan, S. (2018). A hierarchical jammed-area mapping service for ubiquitous communication in smart communities. IEEE Communications Magazine, 56(1), 92–98.

Kent, C., & Rafaeli, S. (2016, January). How interactive is a semantic network? Concept maps and discourse in knowledge communities. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 2095-2104). IEEE.

Lei, C. M. (2018). Hemispheric Lateralization for Language Examined with Chinese Characters (Doctoral dissertation, University of Pittsburgh).

Levy, O., Kenett, Y. N., Oxenberg, O., Castro, N., De Deyne, S., Vitevitch, M. S., & Havlin, S. (2021). Unveiling the nature of the interaction between semantics and phonology in lexical access based on multilayer networks. Scientific reports, 11(1), 14479.

Marini, A., Zettin, M., & Galetto, V. (2014). Cognitive correlates of narrative impairment in moderate traumatic brain injury. Neuropsychologia, 64, 282-288.

Sandgren, O., Andersson, K., Lyberg Åhlander, V., Rosqvist, I., Hansson, K., & Sahlén, B. (2023). A randomized controlled trial of the effectiveness of teacher continued professional development on student language outcomes. International Journal of Language & Communication Disorders, 58(3), 879-891.

Sarubbo, S., De Benedictis, A., Merler, S., Mandonnet, E., Barbareschi, M., Dallabona, M., ... & Duffau, H. (2016). Blunt dissection and direct electrical stimulation reveal structural and functional integration between dorsal and ventral language streams. Human brain mapping, 37(11), 3858-3872.

Sezer, O. B., Dogdu, E., Ozbayoglu, M., & Onal, A. (2016, December). An extended IoT framework with semantics, big data, and analytics. In 2016 IEEE international conference on big data (big data) (pp. 1849-1856). IEEE.

Stella, M., Citraro, S., Rossetti, G., Marinazzo, D., Kenett, Y. N., & Vitevitch, M. S. (2022). Cognitive modeling with multilayer networks: Insights, advancements, and future challenges. arXiv preprint arXiv:2210.00500.

Wild, T., Braun, V., & Viswanathan, H. (2021). Joint design of communication and sensing for beyond 5G and 6G systems. IEEE Access, p. 9, 30845–30857.

Wu, S., & Ma, Z. (2017). Native-Language Phonological Interference in Early Hakka–Mandarin Bilinguals’ Visual Recognition of Chinese Two-Character Compounds: Evidence from the Semantic-Relatedness Decision Task. Journal of psycholinguistic research, 46, 57-75.

Zaharchuk, H. A., & Karuza, E. A. (2021). Multilayer networks: An untapped tool for understanding bilingual neurocognition. Brain and Language, 220, 104977.

Zemla, J. C., Cao, K., Mueller, K. D., & Austerweil, J. L. (2020). SNAFU: The semantic network and fluency utility. Behavior research methods, 52, 1681-1699.



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