Incorporating knowledge into neural network for text representation

Yiming Li, Baogang Wei, Yonghuai Liu, Liang Yao, Hui Chen, Jifang Yu, Wenhao Zhu

Research output: Contribution to journalArticlepeer-review

30 Citations (SciVal)
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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 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.
Original languageEnglish
Pages (from-to)103-114
Number of pages12
JournalExpert Systems with Applications
Early online date22 Nov 2017
Publication statusPublished - 15 Apr 2018


  • text representation
  • knowledge base
  • representation learning
  • network embedding


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