发布单位：成果专利综合科 [2018-05-07 10:23:48] 打印此信息
题目：Combining CNN and RNN for Text Classification
内容简介：Neural networks have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network(CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification. C-LSTM utilizes CNN to extract a sequence of higher-level phrase representations, which are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation. C-LSTM is able to capture both local features of phrases as well as global and temporal sentence semantics. We evaluate the proposed architecture on sentiment classification and question classification tasks. The experimental results show that the C-LSTM outperforms both CNN and LSTM and can achieve excellent performance on these tasks. This is joint work with Chunting Zhou, Chonglin Sun, and Zhiyuan Liu.
报告人简介：Francis Chi Moon Lau received his Bsc in computer science from Acadia University, and MMath and PhD in computer science from University of Waterloo. He joined the department of Computer Science at the