LSTM-Based Neural Network Model for Semantic Search

Xiaoyu Guo, Jing Ma () and Xiaofeng Li
Additional contact information
Xiaoyu Guo: Nanjing University of Aeronautics and Astronautics
Jing Ma: Nanjing University of Aeronautics and Astronautics
Xiaofeng Li: Nanjing University of Aeronautics and Astronautics

A chapter in Smart Service Systems, Operations Management, and Analytics, 2020, pp 17-25 from Springer

Abstract: 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 MemoryLong Short-Term Memory (LSTM) (LSTM), a significant network in deep learningDeep 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 searchSemantic 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 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-30967-1_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030309671

DOI: 10.1007/978-3-030-30967-1_3

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().