Unsupervised Learning for Discovering Language Patterns in Historical Educational Texts

The unsupervised learning has a strong potential to discover latent patterns in historical and educational books with the researchers being able to have a chance to discover the linguistic structure and semantic correlation without the use of labeled data. They are found especially useful in investigating large corpora in which contextual depth and cultural sensitivity needs to be maintained. Nevertheless, the current methodologies tend to fail at treating data in terms of contextual integrity, noise, and distinguishing between overlapping linguistic variables. To overcome such limitations, the research will employ a new model of K-means Clustering (KmC). The structure divides words, phrases, and morphological structures into intelligible units, therefore revealing latent language patterns. With the help of KmC, the proposed methodology will help reduce the level of data sparsity, improve contextual mapping, and effectively identify the repetition of linguistic patterns in extensive textual data. Proposed methodology will be useful in the analysis of multilingual historical data, detection of thematic patterns in educational discourse, as well as differentiation between language families. Experimental evidence shows that KmC enhances the accuracy of clustering, contextual coherence, and scalability, and it can be a reliable way to develop the digital humanities research.

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