Weakly Supervised Query Expansion using Deep Siamese LSTM

Term mismatch is the most important challege in web information retrieval. The term mismatch problem is defined as differences between user queries and contents of documents while referring to the same topic. Query expansion methods deal with term mismatch by reformulating the queries to increase their term-overlap with relevant documents. In this paper, we proposed a query expansion framework based on a deep Siamese LSTM neural network. In addition, we defined the relevant relatedness for the first time and used this concept to label pairs made from user query and candidate query. Weakly-supervised labeled pairs are utilized in training of the deep Siamese network. The trained Siamese network provides labels for testset pairs in addition to contrastive loss values. The contrastive loss value reflects the cost of pulling together similar pairs. Pairs with minimum contrastive loss values are selected and merged together to form one expanded query. Results of our tests showed that the proposed framework outperforms similar word embedding based query expansion methods.