An enhanced short text categorization model with deep abundant representation

• DOI: 10.1007/s11280-018-0542-9
• Publication Date: 2018-04-14T08:05:53Z
• Authors: Yanhui Gu; Min Gu; Yi Long; Guandong Xu; Zhenglu Yang; Junsheng Zhou; Weiguang Qu
• Keywords: Representation; Text Categorization

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.