An enhanced short text categorization model with deep abundant representation
An enhanced short text categorization model with deep abundant representation
复制标题
具有深度丰富表示的增强型短文本分类模型
DOI：
10.1007/s11280-018-0542-9
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发表时间：
2018-11-01
影响因子：
3.7
通讯作者：
Qu, Weiguang
中科院分区：
文献类型：
--
作者：
Gu, Yanhui;Gu, Min;Qu, Weiguang
摘要短文本分类是信息检索、问答系统、MRI数据库建设等应用的关键问题。短文本分类中的数据稀疏性和歧义问题是许多研究的重点。为了解决这些问题，我们提出了一种新的基于丰富表示的短文本分类策略，该策略利用具有长短期记忆（LSTM）的双向循环神经网络（Bi-RNN）和主题模型来捕获更多的上下文和语义信息。Bi-RNN丰富了上下文信息，主题模型为短文本丰富的文本表示发现了更多潜在的语义信息。实验结果表明，该模型与目前最先进的神经网络模型相当，并且该方法是有效的。
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.