Wen, Y;
zhang, W;
Luo, R;
Wang, J;
(2016)
Learning text representation using recurrent convolutional neural network with highway layers.
In:
Proceedings of the SIGIR 2016 Workshop on Neural Information Retrieval.
Association for Computing Machinery (ACM): Pisa, Italy.
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Abstract
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bidirectional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input. The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task. Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.
Type: | Proceedings paper |
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Title: | Learning text representation using recurrent convolutional neural network with highway layers |
Event: | Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1145/1235 |
Publisher version: | https://www.microsoft.com/en-us/research/event/neu... |
Language: | English |
Additional information: | Copyright © 2016 Copyright held by the owner/author(s). |
Keywords: | Neural Networks, Highway Networks, Sentiment Analysis |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
URI: | https://discovery.ucl.ac.uk/id/eprint/1526824 |
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