Enhancing Query Expansion Method Using Word Embedding

被引：1

作者：

Yusuf, Nuhu ^{[1
,2
]}

Yunus, Mohd Amin Mohd ^{[1
]}

Wahid, Norfaradilla ^{[1
]}

Wahid, Noorhaniza ^{[1
]}

Nawi, Nazri Mohd ^{[1
]}

Samsudin, Noor Azah ^{[1
]}

机构：

[1] Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Parit Raja, Malaysia

[2] Abubakar Tafawa Balewa Univ, Mgt & Informat Technol Dept, Bauchi, Nigeria

来源：

2019 IEEE 9TH INTERNATIONAL CONFERENCE ON SYSTEM ENGINEERING AND TECHNOLOGY (ICSET) | 2019年

关键词：

query expansion; word embedding; continuous bag of words; skip-gram; glove model;

D O I：

10.1109/icsengt.2019.8906317

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, many query expansion methods have been proposed to improve the results of search applications. However, many of these search applications still lack better results and many attributed due to query expansion issues. This paper enhanced the query expansion method based on unigram model with Okapi BM25and word embedding using Glove. A Glove model captured the semantic similarity by mapping various words based on unigram with Okapi BM25 results. The results indicate that our proposed method based on Glove model word embedding can significantly improve query expansion methods using Arberry dataset.

引用

页码：232 / 235

页数：4

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