An enhanced short text categorization model with deep abundant representation

Article
Part of the following topical collections:
  1. Special Issue on Deep Mining Big Social Data

Abstract

Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.

Keywords

Short text categorization Topic model Bi-directional LSTM 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments. This work is partially supported by National Natural Science Foundation of China under Grant 41571382, U1636116, 11431006, 61472191, 61772278, the Natural Science Research of Jiangsu Higher Education Institutions of China under Grant 15KJA420001, and the Research Fund for International Young Scientists under Grant 61650110510.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.School of Geography ScienceNanjing Normal UniversityNanjingChina
  3. 3.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia
  4. 4.Institute of Big Data, College of Computer and Control Engineering, Institute of StatisticsNankai UniversityTianjinChina

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