Short Text Feature Extraction via Node Semantic Coupling and Graph Structures - Intelligent Information Processing IX Access content directly
Conference Papers Year : 2018

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.
Fichier principal
Vignette du fichier
473854_1_En_18_Chapter.pdf (260.58 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02197797 , version 1 (30-07-2019)

Licence

Attribution

Identifiers

Cite

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⟩
93 View
55 Download

Altmetric

Share

Gmail Facebook X LinkedIn More