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Original Article
Meaning as Distribution: A U–M–C Model of Contextual Interpretation in Bangla
Sibansu Mukherjee1
1 West Bengal Emerging Technology Society, Kolkata, West Bengal, India.
Published Online: May-June 2026
Pages: 103-125
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260603014References
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https://doi.org/10.1080/15248372.2020.1713456
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2 embeddings. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 55–65.
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12. Heisenberg, W. (1927). Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift für Physik, 43,
172–198.
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14. Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron, 67(5), 713–727.
15. Lotman, J. (1990). Universe of the mind: A semiotic theory of culture. Indiana University Press.
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19. Sonesson, G. (2016). Cognitive semiotics: Towards a new discipline of studying meaning. Cognitive Development, 39, 80–99.
20. Sperber, D., & Wilson, D. (1986). Relevance: Communication and cognition. Blackwell.
21. Tomasello, M. (2019). Becoming human: A theory of ontogeny. Harvard University Press.
22. Vilnis, L., & McCallum, A. (2014). Word representations via Gaussian embeddings. arXiv preprint arXiv:1412.6623.
23. Zhou, K., Ethayarajh, K., Card, D., & Jurafsky, D. (2022). Problems with cosine as a measure of embedding similarity for high frequency
words. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 856–867.
2. Bybee, J. (2010). Language, usage and cognition. Cambridge University Press.
3. Camaioni, L. (2020). Early signs and symbolic communication in infants. Journal of Cognitive Development, 21(2), 135–152.
https://doi.org/10.1080/15248372.2020.1713456
4. Chomsky, N. (1995). The minimalist program. MIT Press.
5. Chomsky, N. (2000). New horizons in the study of language and mind. Cambridge University Press.
6. Chomsky, N. (2006). Language and mind (3rd ed.). Cambridge University Press.
7. Deacon, T. W. (1997). The symbolic species: The co-evolution of language and the brain. W. W. Norton.
8. Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-
2 embeddings. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 55–65.
9. Frege, G. (1892/1960). On sense and reference. In P. Geach & M. Black (Eds.), Translations from the philosophical writings of Gottlob
Frege (pp. 56–78). Blackwell.
10. Gallagher, S., & Zahavi, D. (2021). The phenomenological mind (3rd ed.). Routledge.
11. Grice, H. P. (1975). Logic and conversation. In P. Cole & J. Morgan (Eds.), Syntax and semantics (Vol. 3, pp. 41–58). Academic Press.
12. Heisenberg, W. (1927). Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift für Physik, 43,
172–198.
13. Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5(11), 831–843.
14. Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron, 67(5), 713–727.
15. Lotman, J. (1990). Universe of the mind: A semiotic theory of culture. Indiana University Press.
16. Montague, R. (1970). Universal grammar. Theoria, 36, 373–398.
17. Peirce, C. S. (1931–1958). Collected papers of Charles Sanders Peirce (Vols. 1–8). Harvard University Press.
18. Saussure, F. de. (1916/2011). Course in general linguistics (W. Baskin, Trans.). Columbia University Press.
19. Sonesson, G. (2016). Cognitive semiotics: Towards a new discipline of studying meaning. Cognitive Development, 39, 80–99.
20. Sperber, D., & Wilson, D. (1986). Relevance: Communication and cognition. Blackwell.
21. Tomasello, M. (2019). Becoming human: A theory of ontogeny. Harvard University Press.
22. Vilnis, L., & McCallum, A. (2014). Word representations via Gaussian embeddings. arXiv preprint arXiv:1412.6623.
23. Zhou, K., Ethayarajh, K., Card, D., & Jurafsky, D. (2022). Problems with cosine as a measure of embedding similarity for high frequency
words. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 856–867.
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