News topic detection based on capsule semantic graph

Abstract

Most news topic detection methods use word-based methods, which easily ignore the relationship among words and have semantic sparsity, resulting in low topic detection accuracy. In addition, the current mainstream probability methods and graph analysis methods for topic detection have high time complexity. For these reasons, we present a news topic detection model on the basis of capsule semantic graph (CSG). The keywords that appear in each text at the same time are modeled as a keyword graph, which is divided into multiple subgraphs through community detection. Each subgraph contains a group of closely related keywords. The graph is used as the vertex of CSG. The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex. At the same time, the news text is clustered using the incremental clustering method, where each text uses CSG; that is, the similarity among texts is calculated by the graph kernel. The relationship between vertices and edges is also considered when calculating the similarity. Experimental results on three standard datasets show that CSG can obtain higher precision, recall, and F1 values than several latest methods. Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.

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“…Yang and Tang [26] designed a capsule semantic graph (CSG)-based news topic detection method for the news system. CSG first identifies the semantic relationship among the vertices and edges.…”

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“…Experiments show that the output results are reliable [15]. Yang et al proposed a news topic text detection method based on capsule semantic graph, which has lower time complexity than traditional detection, and the experimental data show that it has high accuracy and recall [16]. Franclinton and his research team proposed an extensible code similarity detection model with online architecture rather than local spikes.…”

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