Enhanced word embedding similarity measures using fuzzy rules for query expansion

Liu, Q; Huang, H; Lu, J; Gao, Y; Zhang, G

Enhanced word embedding similarity measures using fuzzy rules for query expansion

Liu, Q Huang, H Lu, J

Gao, Y Zhang, G

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2017
Issue Date:: 2017-08-23

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Field	Value	Language
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Huang, H	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.available	2020-05-25T19:06:10Z
dc.date.issued	2017-08-23	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2017	en_US
dc.identifier.isbn	9781509060344	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126828
dc.description.abstract	© 2017 IEEE. Query expansion has been widely used to select additional words that are related to the original query words in the field of information retrieval. In this paper, we present a novel query expansion method that jointly uses fuzzy rules and a word embedding similarity calculation. The expansion words are generated using a word embedding method and selected according to their semantic similarity to the original query. Fuzzy rules are used to enhance the word similarity calculations and reweight expansion words. When measuring and ranking the relevance of a retrieved document, the original query and the expansion words with their weights are considered. We conduct experiments on the query expansion in document ranking tasks. Experimental results from the document ranking task show that the proposed method is able to significantly outperform state-of-the-art baseline methods.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP170101632
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2017.8015482	en_US
dc.title	Enhanced word embedding similarity measures using fuzzy rules for query expansion	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Query expansion has been widely used to select additional words that are related to the original query words in the field of information retrieval. In this paper, we present a novel query expansion method that jointly uses fuzzy rules and a word embedding similarity calculation. The expansion words are generated using a word embedding method and selected according to their semantic similarity to the original query. Fuzzy rules are used to enhance the word similarity calculations and reweight expansion words. When measuring and ranking the relevance of a retrieved document, the original query and the expansion words with their weights are considered. We conduct experiments on the query expansion in document ranking tasks. Experimental results from the document ranking task show that the proposed method is able to significantly outperform state-of-the-art baseline methods.

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