Multi-label Classification of Short Text Based on Similarity Graph and Restart Random Walk Model

Abstract

A multi-label classification method of short text based on similarity graph and restart random walk model is proposed. Firstly, the similarity graph is created by using data and labels as the node, and the weights on the edges are calculated through an external knowledge, so the initial matching degree of between the sample and the label set is obtained. After that, we build a label dependency graph with labels as vertices, and using the previous matching degree as the initial prediction value to calculate the relationship between the sample and each node until the probability distribution becomes stable. Finally, the obtained relationship vector is the label probability distribution vector of the sample predicted by the method in this paper. Experimental results show that we provides a more efficient and reliable multi-label short-text classification algorithm.