Reviving Forgotten Languages Through Self Learning AI and Unsupervised Linguistic Modelling

The revival of forgotten languages plays a crucial role in preserving cultural heritage and linguistic diversity. However, traditional methods for language reconstruction rely on manual linguistic analysis, supervised learning models, and expert interventions, making them inefficient for extinct or low-resource languages with scarce datasets. Existing computational approaches struggle with limited linguistic data, dependency on labeled corpora, and the inability to autonomously infer missing linguistic structures. To address these challenges, this paper proposes Self-Learning AI with Unsupervised Linguistic Modelling (SLA-ULM), an autonomous framework that reconstructs linguistic structures using deep neural networks, stochastic modelling, and self-adaptive learning mechanisms. SLA-ULM leverages multilingual corpora, phonetic pattern recognition, and syntactic inference to derive language rules without human supervision by enhanced linguistic reconstruction (ELC). The proposed framework improves the accuracy of reconstructed grammar rules, lexicons, and sentence structures, facilitating the efficient revival of lost languages. Through extensive evaluations, the findings demonstrate that SLA-ULM outperforms conventional supervised models by achieving higher precision in linguistic pattern recognition and syntactic restoration. This approach presents a scalable and adaptable solution for language preservation efforts, offering new possibilities for revitalizing endangered and extinct languages in an autonomous and data-efficient manner.

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