Social Annotation for Query Expansion Learning from Multiple Expansion Strategies

User-generated content, such as web pages, is often annotated by users with free-text labels, called annotations, which can be an effective source of information for query formulation tasks. The implicit relationships between annotations can be important to select expansion terms. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, and uncertain. Besides, most research uses a single query expansion method for query expansion tasks, and never considers the annotations attributes. In contrast, in this paper, we proposed a novel framework that optimized the combination of three query expansion methods used for expansion terms from social annotations in three strategies. Furthermore, we also introduce learning to rank methods for phrase weighting, and select the features from social annotation resource for training ranking model. Experimental results on three TREC test collections show that the retrieval performance can be improved by our proposed method.