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Short Text Feature Extraction via Node Semantic Coupling and Graph Structures

Abstract : In this paper, we propose a short text keyword extraction method via node semantic coupling and graph structures. A term graph based on the co-occurrence relationship among terms is constructed, where the set of vertices corresponds to the entire collection of terms, and the set of edges provides the relationship among terms. The setting of edge weights is carried out from the following two aspects: the explicit and implicit relation between terms are investigated; besides, the structural features of the text graph are also defined. And then, a new random walk method is established to effectively integrate the above two kinds of edge weighting schemes and iteratively calculate the importance of terms. Finally, the terms are sorted in descending order and the top K terms are extracted to get the final keyword ranking results. The experiment indicates that our method is feasible and effective.
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https://hal.inria.fr/hal-02197797
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Submitted on : Tuesday, July 30, 2019 - 5:02:05 PM
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Huifang Ma, Xiaoqian Liu, Lan Ma, Yulin Hu. Short Text Feature Extraction via Node Semantic Coupling and Graph Structures. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.173-182, ⟨10.1007/978-3-030-00828-4_18⟩. ⟨hal-02197797⟩

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