A Multi-Label Text Classification Method Based on Labels Vector Fusion

In massive text information, text classification can better help people organize and manage the mass of text information. In real life, a text often belongs to a number of categories. For this classification scenario, it is called a multi-label classification. Existing text multi-label classification methods rarely take into account the correlation between labels, and lack the understanding of the label semantics. In this paper we propose a method of text multi-label learning based on label correlation, modeling between texts and label vector through neural networks, and capture the semantic correlation between text and labels. A large number of experiments show the effectiveness of the method, especially when the amount of data is large.