Short-Text Conceptualization Based on Hyper-Graph Learning and Multiple Prior Knowledge

Short-text conceptualization is a notable task and popular issue in current social network analysis and natural language processing. This line of work usually views the data as a heterogeneous semantic network connecting terms (in short-text) and corresponding concepts (in prior knowledge base), with complex relationships (e.g., term-correlation, concept-correlation and subordination, etc.,). Therefore, this paper introduces hyper-graph learning strategy for solving this problem, because of its ability for modeling complex relationships. Overall, this paper proposes a novel short-text conceptualization model based on hyper-graph convolutional network. Especially, this model is capable to make the signals (i.e., terms and concepts) to be sufficiently interacted, by leveraging three prior knowledge for modeling heterogeneous correlations among terms and concepts, including: subordination prior knowledge, concept correlation prior knowledge and term correlation prior knowledge. The experimental results demonstrate that the proposed work achieves higher accuracy in short-text conceptualization task when compared with the current state-of-the-art algorithms.