Abstract
Query expansion approaches based on term correlation such as association rules (ARs) have proved significant improvement in the performance of the information retrieval task. However, the highly sized set of generated ARs is considered as a real hamper to select only most interesting ones for query expansion. In this respect, we propose a new learning automatic query expansion approach using ARs between terms. The main idea of our proposal is to rank candidate ARs in order to select the most relevant rules tSo be used in the query expansion process. Thus, a pairwise learning to rank ARs model is developed in order to generate relevant expansion terms. Experimental results on TREC-Robust and CLEF test collections highlight that the retrieval performance can be improved when ARs ranking method is used.
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By analogy to the itemset terminology used in data mining.
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In this paper, we denote by \(|\) \(X\) \(|\) the cardinality of the set X.
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Also referred to as preference learning in the literature.
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Bouziri, A., Latiri, C., Gaussier, E. (2018). Efficient Association Rules Selecting for Automatic Query Expansion. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_42
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DOI: https://doi.org/10.1007/978-3-319-77116-8_42
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