A study on query terms proximity embedding for information retrieval

Ya-Nan Qiao,Di-Fang Wan,Qinghe Du

doi:10.1177/1550147717694891

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.

Highlights

There will be more and more data stored in cyberphysical systems than in Internet of computers
The logical views for the user’s information needs are all based on query terms in traditional information retrieval (IR) models including vector space model,[3] probabilistic model,[4,5,6] and language model,[7,8,9,10] and most of IR models can be applied to wireless networks for cyber-physical systems
We propose the concepts of Query Terms Proximity (QTP) embedding and propose term-field-convolutions frequency framework as an implementation of QTPembedding

Summary

Introduction

There will be more and more data stored in cyberphysical systems than in Internet of computers. The logical views for the user’s information needs are all based on query terms in traditional IR models including vector space model,[3] probabilistic model,[4,5,6] and language model,[7,8,9,10] and most of IR models can be applied to wireless networks for cyber-physical systems. These traditional IR models use the occurrences of query terms in the documents to determine their weights for the user’s queries, and they all adopt term-independence assumption. These models ignore the relations between query terms including proximity, semantic relations, collocations, and so forth

Methods

Results

Conclusion