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LSTM-Based Neural Network Model for Semantic Search

  • Xiaoyu Guo
  • Jing MaEmail author
  • Xiaofeng Li
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

To improve web search quality and serve a better search experience for users, it is important to capture semantic information from user query which contains user’s intention in web search. Long Short-Term Memory (LSTM), a significant network in deep learning has made tremendous achievements in capturing semantic information and predicting the semantic relatedness of two sentences. In this study, considering the similarity between predicting the relatedness of sentence pair task and semantic search, we provide a novel channel to process semantic search task: see semantic search as an atypical predicting the relatedness of sentence pair task. Furthermore, we propose an LSTM-Based Neural Network Model which is suitable for predicting the semantic relatedness between user query and potential documents. The proposed LSTM-Based Neural Network Model is trained by Home Depot dataset. Results show that our model outperforms than other models.

Keywords

LSTM Deep learning Semantic search RNN 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjing, Jiangning DistrictChina

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