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Research Paper | Computer Science & Engineering | United States of America | Volume 14 Issue 2, February 2025 | Popularity: 4.8 / 10
Redefining Text Categorization: A Framework for Dynamic, Context - Aware Classification
Balasubramanian Panneerselvan, Rakesh Ramakrishnan
Abstract: In the contemporary era of exponential data proliferation, conventional taxonomical paradigms demonstrate an inherent rigidity, incapable of encapsulating the stochastic emergence of interdisciplinary domains. This treatise delineates an advanced algorithmic framework that exploits contextual embeddings (BERT, SBERT), high-dimensional semantic vector spaces, and unsupervised clustering mechanisms to synthesize dynamic, adaptive categorizations. The framework orchestrates multi-phase vectorized representation synthesis, cosine angular proximity computations, and hybridized clustering amalgamations, culminating in algorithmically emergent categorical taxonomies. Empirical evaluations substantiate the superiority of this approach via conventional static models, particularly in handling non-Euclidean categorical drifts and ontology adaptation within evolving knowledge corpora.
Keywords: Dynamic Text Categorization, Semantic Embeddings, Adaptive Label Generation, Contextual Embeddings, Natural Language Processing (NLP), Machine Learning for Text Classification, Cosine Similarity, Hierarchical Clustering, Threshold-Based Categorization, High-Dimensional Vector Spaces, Multi-Label Classification, Token Weighting Mechanisms, Ontology-Free Labeling, Domain-Agnostic Classification, Real-Time Text Categorization
Edition: Volume 14 Issue 2, February 2025
Pages: 854 - 858
DOI: https://www.doi.org/10.21275/SR25213131112
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