A study on query terms proximity embedding for information retrieval
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 indepen...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717694891 |
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| _version_ | 1850178854164889600 |
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| author | Ya-nan Qiao Qinghe Du Di-fang Wan |
| author_facet | Ya-nan Qiao Qinghe Du Di-fang Wan |
| author_sort | Ya-nan Qiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c5e98c7d91064bda933aef38bc2027de |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c5e98c7d91064bda933aef38bc2027de2025-08-20T02:18:38ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-02-011310.1177/1550147717694891A study on query terms proximity embedding for information retrievalYa-nan Qiao0Qinghe Du1Di-fang Wan2School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaSchool of Management, Xi’an Jiaotong University, Xi’an, P.R. ChinaInformation 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.https://doi.org/10.1177/1550147717694891 |
| spellingShingle | Ya-nan Qiao Qinghe Du Di-fang Wan A study on query terms proximity embedding for information retrieval International Journal of Distributed Sensor Networks |
| title | A study on query terms proximity embedding for information retrieval |
| title_full | A study on query terms proximity embedding for information retrieval |
| title_fullStr | A study on query terms proximity embedding for information retrieval |
| title_full_unstemmed | A study on query terms proximity embedding for information retrieval |
| title_short | A study on query terms proximity embedding for information retrieval |
| title_sort | study on query terms proximity embedding for information retrieval |
| url | https://doi.org/10.1177/1550147717694891 |
| work_keys_str_mv | AT yananqiao astudyonquerytermsproximityembeddingforinformationretrieval AT qinghedu astudyonquerytermsproximityembeddingforinformationretrieval AT difangwan astudyonquerytermsproximityembeddingforinformationretrieval AT yananqiao studyonquerytermsproximityembeddingforinformationretrieval AT qinghedu studyonquerytermsproximityembeddingforinformationretrieval AT difangwan studyonquerytermsproximityembeddingforinformationretrieval |