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2020 | OriginalPaper | Chapter

LSTM-Based Neural Network Model for Semantic Search

Authors : Xiaoyu Guo, Jing Ma, Xiaofeng Li

Published in: Smart Service Systems, Operations Management, and Analytics

Publisher: Springer International Publishing

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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.

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previous chapter Performance Analysis of a Security-Check System with Four Types of Inspection Channels for High-Speed Rail Stations in China
next chapter Research on the Evaluation of Electric Power Companies’ Safety Capabilities Based on Grey Fixed Weight Clustering
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Metadata
Title
LSTM-Based Neural Network Model for Semantic Search
Authors
Xiaoyu Guo
Jing Ma
Xiaofeng Li
Copyright Year
2020
Book
Smart Service Systems, Operations Management, and Analytics

Print ISBN: 978-3-030-30966-4

Electronic ISBN: 978-3-030-30967-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-30967-1_3