A Query Expansion Method Using Multinomial Naive Bayes

Abstract

Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track.

Keywords

• query expansion
• information retrieval
• multinomial naive bayes
• relevance feedback