Enhancing Query Expansion Method Using Word Embedding

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...

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Bibliographic Details
Main Authors: Yusuf, Nuhu, Yunus, Mohd Amin Mohd, Wahid, Norfaradilla, Wahid, Noorhaniza, Nawi, Nazri Mohd, Samsudin, Noor Azah
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer science
continuous bag of words
Fuzzy logic
glove model
Information retrieval
Information technology
Principal component analysis
query expansion
Semantics
skip-gram
word embedding
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Summary: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.
ISSN:2470-640X
DOI:10.1109/ICSEngT.2019.8906317