Publication

Abstract : Text clustering reveals inherent patterns from unlabelled datasets, a crucial capability for summarizing extensive text corpora via keyword extraction. This study presents a novel two-tier approach that utilizes string kernels and similarity measures to enhance spectral clustering algorithms for keyword extraction from text data. The first phase implements spectral clustering with precomputed affinity matrices derived from string kernels and similarity measures, evaluated using standard benchmarks. The second stage introduces a recursive spectral clustering technique for the hierarchical organization of the data, followed by the application of topic modeling to each cluster and sub-cluster for extracting semantically rich keywords. Tested on the BBC News, AG News, and Reuters datasets, the proposed methodology demonstrates a better representation of text data for downstream applications. The integrated approach synergizes spectral clustering, string kernels, hierarchical clustering, and topic modeling, establishing a robust and comprehensive framework for text data analysis and keyword extraction.