Title: Research on advertising content recognition based on convolutional neural network and recurrent neural network
Authors: Xiaomei Liu; Fazhi Qi
Addresses: School of Information Management, Beijing Information Science and Technology University, Beijing, 100000, China ' Information Center, Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China
Abstract: The problem to be solved in this paper is to identify the text advertisement information published by users in a medium-sized social networking website. First, the text is segmented and then the text is transformed into sequence tensor by using a word vector representation method, which is input into the deep neural network. Compared with other neural networks, RNN is good at processing training samples with continuous input sequence, and the length of the sequence is different. Although RNN can theoretically solve the training of sequential data beautifully, it has the problem of gradient disappearance, so it is a special LSTM based on RNN model that is widely used in practice. In the experiment, the convolutional neural network is used to process text sequence, and time is regarded as a spatial dimension. Finally, it briefly introduces the use of universal language model fine-tuning for text classification.
Keywords: recurrent neural network; RNN; long short-term memory; LSTM; convolutional neural network; CNN; word vector; text classification; ULMFiT; text advertisement.
International Journal of Computational Science and Engineering, 2021 Vol.24 No.4, pp.398 - 404
Received: 20 May 2020
Accepted: 23 Aug 2020
Published online: 12 Aug 2021 *