A study on query terms proximity embedding for information retrieval

Ya-nan Qiao, Qinghe Du and Di-fang Wan

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 2, 1550147717694891

Abstract: Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it is a very useful information to improve the performance of information retrieval systems. Query terms proximity cannot retrieve documents independently, and it must be incorporated into original information retrieval models. This article proposes the concept of query term proximity embedding, which is a new method to incorporate query term proximity into original information retrieval models. Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.

Keywords: Cyber-physical system; natural language processing; computational linguistics; information retrieval; query terms proximity; convolutions (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:2:p:1550147717694891

DOI: 10.1177/1550147717694891

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