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| Query Expansion Combining Copulas Theory and Association Rules Mining |
| HUANG Mingxuan1,2, Hu Xiaochun2 |
1. Guangxi Key Laboratory of Cross-Border E-commerce Intelligent Information Processing, Guangxi University of Finance and Economics, Nanning 530003
2. School of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530003 |
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Abstract The Copulas theory is introduced into the association pattern mining of text feature terms, and a query expansion algorithm combining Copulas theory and association rules mining is proposed. Firstly, top n documents of the document set returned by the query are extracted to construct the pseudo-relevance feedback document set (PRFDS) or user relevance feedback document set(URFDS). Then, the support and the confidence based on Copulas theory are applied to mine the feature term frequent itemsets and association rule patterns with the original query terms in PRFDS or URFDS, and the expansion terms are obtained from the patterns to realize query expansion. The experimental results on NTCIR-5 CLIR Chinese and English corpus show that the proposed expansion algorithm effectively restrains the problems of query topic drift and word mismatch, and enhances the performance of information retrieval with the quality of expansion terms improved and the invalid expansion terms reduced.
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Received: 06 July 2020
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Corresponding Authors:
HUANG Mingxuan, master, professor. His research inte-rests include data mining, information retrieval and machine leaning.
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2021, Vol. 34 
