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Consulting and Forecasting Model of Tourist Dispute Based on LSTM Neural Network

  • Yiren Du
  • Jun LiuEmail author
  • Shuoping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

To fill the vacancy of tourist dispute in legal consultation resources, the consulting model of tourist dispute is proposed. The legal consultation model studied in this paper is based on the Long Short-Term Memory (LSTM) network. In terms of natural language processing, the Chinese word segmentation tool jieba popular in Python is adopted, to realize dialogue through the sequential translation model seq2seq and solve the long input sequence being covered or diluted with the help of Attention model. Finally, Google’s second generation of artificial intelligence learning system TensorFlow based on DistBelief is adopted to train and optimize the model, so as to realize and train the forecasting model in this research.

Keywords

Sequential translation LSTM Tourist dispute 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Zhejiang University City CollegeHangzhouChina

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