2019 Volume 27 Pages 61-76
In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations. One way of alleviating this problem is to reformulate the query via query expansion. However, finding good expansion terms for a given query is a challenging task. In this paper, we present a query expansion framework, where supervised learning is adopted for selecting expansion terms. Upon retrieving tweets by our proposed topic modeling based query expansion, we utilize the pseudo-relevance feedback and a new temporal relatedness approach to select the candidate tweets. Next, we devise several new features to select the temporally and semantically relevant expansion terms by leveraging the temporal, word embedding, and sentiment association of candidate term and query. Moreover, we also utilize the lexical and twitter specific features to quantify the term relatedness. After supervised feature selection using regularized regression, we estimate the feature importance by applying random forest. Then, we make use of a learning-to-rank (L2R) framework to rank the candidate expansion terms. Results of extensive experiments on TREC Microblog 2011 and 2012 test collections over the Tweets2011 corpus show that our proposed method outperforms the baseline and competitive query expansion methods.