An enhanced short text categorization model with deep abundant representation

作者：Gu, Yanhui; Gu, Min^*; Long, Yi; Xu, Guandong; Yang, Zhenglu^*; Zhou, Junsheng; Qu, Weiguang^*

来源：World Wide Web-internet and Web Information Systems, 2018, 21(6): 1705-1719.

DOI：10.1007/s11280-018-0542-9

• Short text categorization
• Topic model
• Bi-directional LSTM

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.

• 出版日期2018-11
• 单位南京师范大学; 南开大学