Incorporating knowledge into neural network for text representation

Text representations is a key task for many natural language processing applications such as document classification, ranking, sentimental analysis and so on. The goal of it is to numerically represent the unstructured text documents so that they can be computed mathematically. Most of the existing methods
leverage the power of deep learning to produce a representation of text. However, these models do not consider about the problem that text itself is usually semantically ambiguous and reflects limited information. Due to this reason,
it is necessary to seek help from external knowledge base to better understand the text.
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.