Florian Boudin. 2013. A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction. In Proceedings of the Sixth International Joint Conference on ...
Degree centrality is calculated based on the degree of the nodes which represent the number of edges connected to the nodes. In GoW, the degree centrality of ...
It is shown that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best ...
In this paper, we present and compare various centrality measures for graph-based keyphrase extraction. Through experiments carried out on three standard ...
A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction. IJCNLP 2013 · Florian Boudin · Edit social preview.
A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction Florian Boudin In Proceedings of IJCNLP 2013. This code is frozen as of the version ...
Jan 25, 2014 · Moreover, centrality measures are very fast and easy to compute on single-document graphs, as compared to other more complex unsupervised.
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In the graph theory centrality measures refer to indicators which identify the most important vertices within a graph and that approach is used for the task of ...
We analyze nine centrality measures (Betweenness, Clustering Coefficient, Closeness, Degree, Eccentricity, Eigenvector, K-Core, PageRank, Structural Holes) for ...
[PDF] Keyphrase extraction using graph-based statistical approach ...
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This research paper introduces a novel approach to extract keyphrases using a statistical approach based on graphs that incorporates degree centrality, TextRank ...