Leverage Heterogeneous Graph Neural Networks for Short-Text Conceptualization

The conceptualization of short-texts is playing an increasingly important role in text comprehension, social media processing and other applications. Generally, this problem could be modeled as a heterogeneous semantic network connecting words (in short-text) and corresponding concepts (in knowledge base). As a crucial step in short-text conceptualization task, learning interactive relations among words and concepts has been explored through numerous methods. One intuitive method is to place them in a graph based neural network with a more complex structure to capture inter-concept/word relationships. Hence, this paper presents a heterogeneous graph-based neural network (HGNN) for short-text conceptualization, which includes semantic nodes with various granularity levels, mainly consisting of basic- semantic nodes (e.g., words) and supernodes (e.g., concepts). The proposed model could provide a flexible and natural modeling tool to model such complex relationships and capture more expressive and discriminative concepts, by leveraging mutually reinforcing strategy on heterogeneous correlations. On the other word, it is a beneficial attempt to introduce different types of semantic nodes into graph based neural networks for short-text conceptualization task, and we conduct comprehensive qualitative analysis to investigate its benefits.