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Word Embeddings-Based Pseudo Relevance Feedback Using Deep Averaging Networks for Arabic Document Retrieval  

Farhan, Yasir Hadi (Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia)
Noah, Shahrul Azman Mohd (Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia)
Mohd, Masnizah (Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia)
Atwan, Jaffar (Prince Abdullah Bin Ghazi, Faculty of Information Technology, Al Balqa Applied University)
Publication Information
Journal of Information Science Theory and Practice / v.9, no.2, 2021 , pp. 1-17 More about this Journal
Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.
automatic query expansion; information retrieval; word embedding; deep averaging networks; pseudo relevance feedback; Arabic document retrieval on TREC collection;
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