Incorporating knowledge into neural network for text representation

In this paper, we propose a novel framework named Text Concept Vector which leverages both the neural network and the knowledge base to produce a high quality representation of text. Formally, a raw text is primarily conceptualized and represented by a set of concepts through a large taxonomy knowledge base. After that, a neural network is used to transform the conceptualized text into a vector form which encodes both the semantic information and the concept information of the original text. We test our framework on both the sentence level task and the document level task. The experimental results illustrate the effectiveness of our work.